{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "id": "39a8de56",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "#         print(os.path.join(dirname, filename))\n",
    "        pass\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f138ecd9",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: joblib in /opt/conda/lib/python3.10/site-packages (1.3.2)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6e5da00c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T13:52:49.194173Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# input folder\n",
    "from pathlib import Path\n",
    "Path('/kaggle/working/oral-diseases/').mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e69d3e12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T13:52:49.220114Z",
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     "shell.execute_reply": "2024-01-07T13:53:06.506754Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/kaggle/working/oral-diseases/caries')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import shutil\n",
    "import os\n",
    "\n",
    "# Caries.\n",
    "# The source path\n",
    "caries_file_path = Path('/kaggle/input/oral-diseases/Data caries/Data caries/caries augmented data set/preview')\n",
    "\n",
    "# The destination path\n",
    "caries_new_directory = Path('/kaggle/working/oral-diseases/caries')\n",
    "\n",
    "# Copying data from input folder to working folder\n",
    "shutil.copytree(caries_file_path, caries_new_directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ae691f05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T13:53:06.532010Z",
     "iopub.status.busy": "2024-01-07T13:53:06.530061Z",
     "iopub.status.idle": "2024-01-07T13:54:13.295084Z",
     "shell.execute_reply": "2024-01-07T13:54:13.293848Z"
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     "exception": false,
     "start_time": "2024-01-07T13:53:06.519887",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/kaggle/working/oral-diseases/hypodontia')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculus\n",
    "# The source path\n",
    "calculus_file_path = Path('/kaggle/input/oral-diseases/Calculus/Calculus')\n",
    "# The destination path\n",
    "calculus_new_directory = Path('/kaggle/working/oral-diseases/calculus')\n",
    "# Copying data from input folder to working folder\n",
    "shutil.copytree(calculus_file_path, calculus_new_directory)\n",
    "\n",
    "#Gingivitis\n",
    "# The source path\n",
    "gingivitis_file_path = Path('/kaggle/input/oral-diseases/Gingivitis/Gingivitis')\n",
    "# The destination path\n",
    "gingivitis_new_directory = Path('/kaggle/working/oral-diseases/gingivitis')\n",
    "# Copying data from input folder to working folder\n",
    "shutil.copytree(gingivitis_file_path, gingivitis_new_directory)\n",
    "\n",
    "#Ulcers\n",
    "# The source path\n",
    "ulcer_file_path = Path('/kaggle/input/oral-diseases/Mouth Ulcer/Mouth Ulcer/Mouth_Ulcer_augmented_DataSet/preview')\n",
    "# The destination path\n",
    "ulcer_new_directory = Path('/kaggle/working/oral-diseases/ulcers')\n",
    "# Copying data from input folder to working folder\n",
    "shutil.copytree(ulcer_file_path, ulcer_new_directory)\n",
    "\n",
    "#Tooth Discoloration\n",
    "# The source path\n",
    "toothDiscoloration_file_path = Path('/kaggle/input/oral-diseases/Tooth Discoloration/Tooth Discoloration /Tooth_discoloration_augmented_dataser/preview')\n",
    "# The destination path\n",
    "toothDiscoloration_new_directory = Path('/kaggle/working/oral-diseases/toothDiscoloration')\n",
    "# Copying data from input folder to working folder\n",
    "shutil.copytree(toothDiscoloration_file_path, toothDiscoloration_new_directory)\n",
    "\n",
    "#hypodontia\n",
    "# The source path\n",
    "hypodontia_file_path = Path('/kaggle/input/oral-diseases/hypodontia/hypodontia')\n",
    "# The destination path\n",
    "hypodontia_new_directory = Path('/kaggle/working/oral-diseases/hypodontia')\n",
    "# Copying data from input folder to working folder\n",
    "shutil.copytree(hypodontia_file_path, hypodontia_new_directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1bb0e210",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T13:54:13.321533Z",
     "iopub.status.busy": "2024-01-07T13:54:13.321162Z",
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     "shell.execute_reply": "2024-01-07T13:54:13.331206Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hypodontia',\n",
       " 'gingivitis',\n",
       " 'caries',\n",
       " 'toothDiscoloration',\n",
       " 'calculus',\n",
       " 'ulcers']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The different classes we have from the folders\n",
    "import os\n",
    "build_dir = \"/kaggle/working/oral-diseases\"\n",
    "caries_dir = os.path.join(build_dir,'caries')\n",
    "gingivitis_dir = os.path.join(build_dir,'gingivitis')\n",
    "toothDiscoloration_dir = os.path.join(build_dir,'toothDiscoloration')\n",
    "ulcers_dir = os.path.join(build_dir,'ulcers')\n",
    "hypodontia_dir = os.path.join(build_dir,'hypodontia')\n",
    "calculus_dir = os.path.join(build_dir,'calculus')\n",
    "os.listdir(build_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a1afd31f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T13:54:13.351207Z",
     "iopub.status.busy": "2024-01-07T13:54:13.350217Z",
     "iopub.status.idle": "2024-01-07T13:54:13.364104Z",
     "shell.execute_reply": "2024-01-07T13:54:13.362701Z"
    },
    "papermill": {
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     "end_time": "2024-01-07T13:54:13.366699",
     "exception": false,
     "start_time": "2024-01-07T13:54:13.341697",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Showing the total number of images from each class/folder\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.DataFrame(data=[len(os.listdir(caries_dir)), len(os.listdir(gingivitis_dir)),\n",
    "                   len(os.listdir(toothDiscoloration_dir)), len(os.listdir(ulcers_dir)),\n",
    "                   len(os.listdir(hypodontia_dir)),\n",
    "                   len(os.listdir(calculus_dir))], index=['Caries', 'Gingivitis',\n",
    "                                                                    'toothDiscoloration', 'ulcers',\n",
    "                                                                    'hypodontia', 'calculus'],\n",
    "              columns=['Total Images'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0e62bd95",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T13:54:13.384909Z",
     "iopub.status.busy": "2024-01-07T13:54:13.383767Z",
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     "shell.execute_reply": "2024-01-07T13:54:13.395869Z"
    },
    "papermill": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data.to_csv('data_distribution.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f86b9274",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T13:54:13.417231Z",
     "iopub.status.busy": "2024-01-07T13:54:13.416923Z",
     "iopub.status.idle": "2024-01-07T13:54:13.437538Z",
     "shell.execute_reply": "2024-01-07T13:54:13.436711Z"
    },
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     "duration": 0.031248,
     "end_time": "2024-01-07T13:54:13.439528",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Images</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Caries</th>\n",
       "      <td>2382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gingivitis</th>\n",
       "      <td>2349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toothDiscoloration</th>\n",
       "      <td>1834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ulcers</th>\n",
       "      <td>2541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypodontia</th>\n",
       "      <td>1251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calculus</th>\n",
       "      <td>1296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Total Images\n",
       "Caries                      2382\n",
       "Gingivitis                  2349\n",
       "toothDiscoloration          1834\n",
       "ulcers                      2541\n",
       "hypodontia                  1251\n",
       "calculus                    1296"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "974e02a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T13:54:13.456941Z",
     "iopub.status.busy": "2024-01-07T13:54:13.456457Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Sample of caries\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0151ca40",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7b403aae7b50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GBBH8Qq4tZdG+iQUoAhgxo+DH3DpHBv7Y8x3PtRWhT1dcmI8DxONYzIv6MQGUz02ANBA4Bbcfox/iLgE89jG5OtzKMAWtHcZ30hBYwEQQqPsF7j4Ry2zJjqqg8L6bBYR3TZlt9bndMlPiUWbtVWaMdgSf9oyzdc1OaKSBdUSRUlxH/9jymUvMRwhaib8TZF2nvyqA2EGt6FM3v45yZLW3sZqz2a+Ped6P7ZpWd809ewQyAgAW00EEmAUdkJGauZkpBsuVsRYAI7Ke+BBsXR4ekVTZnM4pMx8m6QS0RmHwoviaAqgZGvHYyLhN8+FySTv1c46HquLzYqziXWJ+wjKFQ3ev0J/Ax0MCjOi6s9iLCAxtMWbjStq7zg9pPFW4fw4C0OfFzk3wWCwfkll9YmuRpVjzwhKndDAsULXSUWMBde+Trok4Fc4jnYwP8X0FSLI4HJmBXXlgYApltetwff4c89fJuccA9zUl8bvhXnmsfWLBxzVmcvz9OBFvEwcyHW9EScdF9ZSgLe0akzv2r6Jqc4jO13gaSut9jRBTOBYBYVpRPFL8AzMBJupN9GtCYbKGHK0j80qpcSA6FtNAmLbmjJ5M+X0EIB0euco2wHj28VhB1MzQZ3NmXmoolDTI5Mn2ncVfRAyGWzLKbcTcIGNU5qgCxt00QgboKpDx8bJ3MbNuMEoWjDFSmLhgKYxUrE8BnrxvPq9XgGezibiq7dRrIK+hY3eZJ5HXPoKNy7+n5hpr6edx6jB9NwtqBzhtEmIlCDGexwSdjV+6I+raT3Z9IThLP1xgxBCJwQWbNy5T6m9ynKsYKx8WmbEFARgWxIqSvTOtIUtDPmrM5bkENl9F4AOUbpCIXM0077CyIS0QEXPhg1QpwH7j4s6Y+1L7JsEfquDXZO4CegxwEjyVnHGkYe9LgCiWjONph34YkIDRCCHrAtnXdj3jlbEWXKnQI6mp1cuVJRHNVnLLIQGQ1iw93N1gSU26xhihc5XgYhio9WPVhaMM2ukv6CqWVMDF/DL6Vp7d1nkrbim9b0LMyp9nGebAzNd5mfoAeo+3o2L6cb0O3j6x4ONN2seNC1HiOpwrSRj69vigHi0Qj/12tHzEMY8/0pNtiIDARduqVOaGh3ofp9oZeHCkvJaAu2hVjyjfEiCSC/nYUvZKLCQUreXyHtnv/HZmviHLOK8j7AtbD2BoXnoDomZGBHgihVeMV6nzUYV+pAaLRIbK8LTaaQ6VI/A+put6psqEoARhZfECTGPb9TmppE1LGbcDsHgMPBxTb0NbMy37qikWR5rJa9dr8kEjncDHcV0QgSJwzsXMvIb8jCmbrGpz7ksXwbATWNxT5ccVSYEGEAPUCsPW+9fh4ujuJU0LfO7tsozijjCgOJ2j1xilKNwRpGmfD+MTf6oZ32VSnR2iYgmbZO10lPXa3+vz1ue3Z3LgWzri4G1qVwC++CA+wl8FcvGcIoIhbIGbMVu2WAngtE45fY0p3sC0J7vsGCP7e1BYco4KjyMPEE5a8hTlWbs3GMHJowGvAcJxOxJo5HjXrumQzuAjXz6+CT70IDEg4hMu4VrLvqQyd5z2utwuAQgu3utx87EyPWvKqDL69FgA7dyuAZC38jiU9okHH09ZH54yHT1mnprOc1F95BevASBvOthX+6Ar5+oUv8l1q3CqwUJP1dF3NF/vMVuJLp/5+OxEFGj7sf7Ey79XNDTd0y8b4z7dr6zca7jFA8QC6eiBDj6YjX8NAawQlVRAYNcll272TkSgUoF0mOSTkcI4Amo7heYV5mXvz3wbY5BuafFsGdNGm2mEzmAPYOg4/i6cji4h+Dhfaa7N5LE5ZwAs7iHHfwY1ZV5MGPvfs0i0VUSHazsdoNzXxwQuRoswlPgn7x2fMgbH56i6+UJY2jUPUxHXiH/J4zgSugRpFQF2nIdrayjSiEUs2ySrbV488wQGvG+EJqbFU46liFphVJhRGUNBZs9MPwVXCSEZ5YaLMhaTVyRcHadqjat0dTi8AkyfT3dd+FRq1y1AtghALrQGAK33EPA4jLldZOJXvhZmcGuWslKjJdzI5LETHCMEWLAmOQAwywdYU2+pGRih6AN7AT9U8IF4BxL8O20CALqksdv5f9DCFUkg87e1bs/rrA/XlOFrPD3Hs6Soy9XexPFPKeTX5Mrr2icefLxNO1oa3sYkJMCF2+XJ49/i2vUcd71cE+7OHK4WDdILvP7aR6ahV5+0o/JtufgMJGq/jteaTir3vwAggeyL1mVrsoKC4/2uZSC5wCcBiAsTL4ISIlP8RBQCK2MS13Zh7FpJAUkTEHDLBZyRms5etF8q/fPLR7yHFOAR1yIwWCuhPrVWxUcQ8MCza9aR+f3AjOr84MDYS3+P1xMpwtKZ0mtpI6/l7+xCwMeV5kC6AoGvMtFI2wRmMKrmgsKoBUnR14S8xeCYajoBFEl3iAvuawBQXKIWSRzuARMsFyMiWS2krnsX9AEep/VbbgEf55QMXows3LjTfB9EWqX3cl+qEs6VNz+uroOLy1xZ99BnHLW4nfXP40ccpPnv1XIXsbIHIOy/VSuDfkdTPFX0za8hEkQjZuUVVPrXMY5sGQiiUCLUkUeNAbZ4MgMpccxFnwTNqkdHqBs5rQOeEVOJMvkjgOAqQH1zFnlNqZ55wCVPPh5zbd4qaKrfv2n7uBYPb5948FHr9vt7RdFHwnRt5Np1jjEhmeI4ANR0vzdr1ybqOCFXgwDFCWv27RNpsRhF055emoJQzdDlnm3WCCU/2g3n+8a6cwZaC9PwCJkb4s6OqyAlhNEBaPhxrc2M/UIM2J9U71OP5bxai9/dOuAPAfgWJjzU3NEIwNAS3bCUVYoATi8tXDJi4LHihZG46BpZ0yPqcQjAJBikfvdx3mOIqQJGH49q7SjWj9hPiAGREcWMji3GRKya58OeboE6nJLma71XQ+9ZzXY/b0HrUd3U2vD8T8DGKAuojQJMQBk86/1xYUmg2LODiDDGSMHigxPXoaTVyhSlAn8PBOYoa+7nRBKlgRqJ6zAIttdPEVCxbpxCBYBVEqWm5bMdIEWGVBn7MOiUdWsoqow/5qqeSL7AXoHVxyKgURGS9lmrwAJoNAUjB22ULogcxm/WIXKbgNaiRP+ICqw2pGXOmoHD+Nsq0nIBCdXy5fdzOhD7PCIgs9C0z1153qmAHARjuwQd9fmp0A3BE1atXD1qem3hROT8Pl0cCUx0sKqVxOndp0l4B1HDKBa2cMNdkTtM5rSo82WHL+uC5i63eKZyHZt3gpfL10kcYwDkoC2Bir+XIbXhaXltB6Gia8qf/1JhSb7vK+Cqy/Zwnj9HfX/b9okHH96uWTKOZqfZvD8P0lMDJJ4iibcfyDfRBOfvZ20y+j4dUXh2+WPaD6IKvHKP+dwDEjkg5UrTQYBS+0vJcA/MVeWDzGNPDloSUbtwOcIQqp9svfkCcBgQ//oi82sJyndQrQQW5xHBAvmqbg9nlhUIKnNpSG2JrQKiTHvB+G8imdnizIPKNYOxOugoQjYGVObx8Bb0ZM/mYzKEL4R5XjeZuk+Da5pTHAcOzN36NDHn2ofar8B/EnQkImhWBfbi2aFS+TFmlo9oioRpnCnQ7HyqtJJ6vSDHz6/jNKTwLgdQ4qgU3l55Qe+faytYfNWyD3c/9n967uMYxljSVR5WrwWk24gO93XQd3Ef+2fqm9QRSHAQ5xKBjD7ScpP3S/doPkvdn6TyrqqAHHlhnZ8KtALEhpLI0/lHfl7HsYymKnYyn+euxlar3/j4iNNa0qr+wvEMPg7MUqweSoQafOpLUIBmpjf7XYBM361zO1jZkvM5Sh5JzjyC0A9rJdj1tTVUY5yOvxcen8MXNFgtI3mreRwvnuMJ4PGLyu1yDWi8ziT01O+1UJG31ggQuiii/DZ9vAZ8jveJPriWEBAYpTjXLFSO/Q3/5WvG4Ho/qTDmS+K6RMOXvx+Fy0VQov3WWkMj22vFlQC5vI+vOfeRx2IoViu3eCi+kGRiBj7cmklDILwnMJgqhkq6Tg5MWzVsCSbsAEqGpePutssjS2w45dYUT5mtzecyNpbze0x9Se2C4amSV0ypfh8P4oNZbcq8TDu+ljnwYNkLBnMhGC+/CyY8NRWJWRY7W6WHauVzUe7uFWfQkwaPvFYUayvCzG82HMDMuMzuIwFe630noTTBDztOBBT7tMy/4bB2rwnP+vlR4IGZ2YPUSnQxD/loFzxoMGMUi9SFYD4InqAxUQDtx1crRV0HPm9kfYtdXo+g6gpVXADZw28Ofivv8HtWHteQ2z8c+clxHO1h7Pe0ih95l9O/gpEZeM/gLa0CANJy4WvNQJQWMzTbh29qyQlOeoCUCpV1atgsnqHLkQacixdg84c3GhcPVPVxP4ztNTlwTcHO57xm8Xi9PH2s/UIAR22fWPDh7TFBee24YzsSff2eDsjweJ2rpvArk3tx3UeIwN8lvgPctXJtb4L63E89/+sn3sdg/k4mmnZNxxll/c7WRPTLCTffa381k6Zo+qm+Zf+lPseV56mDZAIy7iXH76EWj2GWCuELEOIuF46siuyLMNvutZYOSe6+SHcJCwfNkFktsnZKmVNOIVPnK6wfx03tkOVQLgWa3RvJ/IUwne/AhjAPmacekj3XJCALI6qg8ChAj2LwOFt5lGrFjSi2lBfM/YT3wzroZmQpICHuHfQx04Q4LfkhfozTizFxKZ27RmHk/aECOq4qjbZu/GxD75PNJebDv5vn1x47LYEFJCkgQ7mHBB3M6y3dYfO1W9SxqOcCcgHkaof8mBwj/UxmYWzSYkt6pf2ZZuZxLXwAl/e6pE3rk7mAroKXK3xc57kIz8pLcBSkeccA9UFrMwyNsah9j8hQBRbsAMT5iXUo3UEtqrgSaKoP4CUOFExw0FoAUZDuQEPu/rJU76b9EAnn4jyuDupxnf/PQOM68LjWfOTeBFQcleLj92/SPtHgo6Jn//ux447tUVOvM94WogQ+7FVWfpzmGvRT902BTUGINa7lGop3zeStkaox5kIayL/SZBf9ib7m6f53BRmV+c/8w4FLVgskoqn8t7pAyk3AZfE1+NEiElu3e9aKCtZSHMzehVlTX91acCVlNMa1AAAHGEQUW8D7vT3m47ihG3maoMWDVAbv87T4Vt1w4aGWiOOATQCzfBf39/47gzQ5ewFAy/meRi2S5mW/5yQkBBcFvRKglMnXX/FYc9HLxtwZYlkB5RmN0Kb1XASvPi/muAJ/Nqp1OJJWg2StmNSxT8p0g53mCIU2TGE1OR5ylQkUQTUJLzoCj8NYuUJsfh4Xhek3zPv7XjOqUGsnmAf2fbuIY+u923pZYqqS7veCrA68MEBX0oELnCgwby46Zr4oBObddSD8qCbNDGotsnjqMXVfHgBzttaBtgUSlgIUGgUAEneeVZpJcEs0WwlyLc2xT/VuLgeyKJpPoG4uKRCNyyHNiHFLEQxEBBAhp1HfHXimP7WCUZxHImoNId2pV0u+T5sc+OnRH/IuF0tr0o2P75sBj7z+deDxlDL+ixJ8AK8fNDkQ9/G8x5CZSBbDucZc63Wvtaeu+7oJcKK+ZiV56lnethUFDtfSKvLej43t8e+DFi3X+6Z7oNhOowCEZnDFIxlfs4XYbTE36nHzjK9wCwSCAUW5c89o2UfIj+k8MQ3C5jkEnAMaHlF/wfdJYAMMvO96HZHop3UhTNvHmJCjqyMqYVqfiGe6TO0sB/0aaGLiabwrnXmRMf8+GTBMQGVwnRTG63tnVY05rm97jvg81b1oJnrxvvtzXFmvLFo108fSAUXUq6n2hKPwKcdftkz/ntaROJrwd8QxZEyfQYf6VemDjyge26+FLSNKgRSBJC2EzuNFuAB5yWtmp2z8Z6ASBb1YFSItDOa7KgMsDoCzkqoqJKQgvl+OV6U/D171OjYkfHFMvFsP2dJOKwCOyqXlaaZzD2tgcjeVdqTtWC9+LZGJGx2Vrnqf4XtSX6GNlPXXUlEZ812mHup50Bgwt0wIGc8wmmKjD5JmGXPGy8UTBqxuBiW/F0o6pUZawZTUTe0VXWEABgS4w1Xo0go+7eLcKPa/ctqa+fKbAQ9fK29qcT++vF9v2j6x4ONyAK8PwOuE9NE85Oe42Tw0vbIA3xS9PYb4ngIggabx+MReu9618fhutKfuee03F/Yw94cuwtRymB186PhntocFv1nUeMyNCGTXctZcNONhZdJ5mMVjcETtx3lWJGzSsg5CPVw1IgGSHHxooJi5XVoDUfrCFRzNQMGtVLOfu+zA6sKjDpjLyOzgDDrq+8GEOmlY03yUWJCGYOxqxg1pWXdcR2VaooMQ91NAV8bs+AyH+8+aJgJ4hHCheS48mFEgj173MZBeGd9Rwz42Bx4UNVbq0Fu2QcvPCRa0DoSfkJk/fk+3Nh2uWbCPCz2UjCMHKjosnJVq4RqulFgltxYQRCisI/mcZXzr87eqZQPEs8Iz0RQM4BfLYRQEw/UxPYIP71Oz2JZjDZqjy6XO7QVwTTQXPHoGtZdZjbXNeLT2fQYfhx7C0LrNRelfcbuAzGViNBB8rKxLvzBTiWmh+NKAhsRzuoKjZdUBbhLxH0QJ7LxFsLAXW4s6iY/JipnZHNeUXPntdUr1EXz8orR8vMkx1wjaW++qUYfZFzDTOYf6QuV6bzKIT6G8R5kfKfMAqlC4njZ8ISjxdpP77W5zZccr8xJCiUx7s708kM+rS1aCuUMoYg8GM4QHGpEFbeo6571oX/batw08BvbzGSRAR2oRsXeEVyiNCoOuyQv24ZYSUgHNAGt9TQ3wY8HYdrMVIFzB18Y/hGhJea3mz8rE/Wwq/4UJNYJMZWLSIgLb9WSaAzKwBaoCRVAZzAisFHptzF2bpnCu3OlphcFsBfDaED7H19qFVn38jq0y6fG4j4mrHfC9CVhvrYUgdm2WA6iZxmpUSrCUXPGgYBX47urx/FqnXVJC1+dwwgU5icdXI9K+C1A3ShQDZhIBmA641fIBYS2AB9gYsn9ULX04IN7jml44q3HGMyk9VTCr+6+Q5Lx4/I5bXWpF0wRXV/iujR578T5bd3HmND4uDkdcLq5lp3QDYwxMQdgC3Yn4USVPXKmMoShNHvkcHcrjSdNoUaxYMcdkgMSeUcGHrWqjLwcwcW0gLRwEgJoCl97CGgfygFQJUvPzwqpiS6n3IqtErbePW3Webkc9aPrtoOhfs9q/bfvEgo+ZMc1Dcs01Uc/z92va0fH6zJzmelxnqW/i/6p9udbfOMf77prVU8ce+nkEO6+bfF98KfqBx4TGk9e5wmTq/av2BSloODTIuFD5TrlyORNiPmZNezUNQGbrQrxYq4Xu244G87uSKFN2V0fZm4XMTOqauwzVYnprqjmUfZ1kSFo/gNDJhRgibTamH+av9pGIsi8OPpwmDy8BpiJ3F0IbqZlczAcqvc/go54jLsRyOi77bT9EH6a5syEOtHTZngIfELlaVdWBkQ/MtfF9Csy/jdYVxzeKe8WlaXq78ocjUP+sA2LyYuYBdQ5M5jJqPJIdVcCHH8wEl3A2PoBaTEr2Cosp6CPvzxlcDYP9KcyN7o/ryOfkMNYauyPTVg7z89VhsfMSnWnGjIjxu8PcFfAhANIYd7DYlLETyATOAbH1eH3Oq0uRyvAem1qqilCttOdgiShTaCutSPku5r9Z3xxgpD2vgogEH2Kqml6DYfEtLWOSZvDhgE5AxCBqaK2k1vsifbLNyvWbyNN63NUrPrFOH2ufWPDh7Uj013yJTz1wdbV4u5Ym+nHa0ST1uuuFibwg1cf6+6b3Pn6u/dHFYabDYLKPp9nWMfHnqRr9tbl4qn/1VykL1TXJiJkAQKzFsvZ9g7Su2qxpYmPkPipepEv2Ad537OfNtP+m6b2eyiiCYTEbxKJpbc0i+Vl/AwBYoKmbowXQ4mIH0EMdWS4eNGWeHMdpmgvPmDkGrl6xdDW5nAcFAVVLPcyb/lHmQ4K8CBSmadeyqwvMWW0FAMPux0PnxTXe4N9EWKyKwjU6ulhbpolpfJXHflwCrATJj7cj6H1dq2tytgwdBaILei9gNWJcIMg0aXHBnr9VN0peJ10lepi6M4bM1rt6f7XI6nfDLAF13WXshc0fmyIyzkG7np01eIDgQkmzuKpVzq9T56wCOGlzfZjaFzzCOyAlfo7msvPH9pgyM/EWfybnmSIlwNuG4FD7pt7vWBTNLQZ+XO3La8kpaAhJpD7/Vsk01onRNZn1AsCF6ylcMuWYZkHYFJ0FuOPC8uFWvkxC6CACes+icWrZO9wvHyaA8mPr6G3X2cdtn2jwcQ1xA5cmID+2/u4TVK9zcU0nyILEn6LExwR+BURHIHLVSgIHBQg+6Cwxj8lXudF8PZUGV01lIhk74Tc58oHQqkI71pcTZ76s19Pxtbf+vDqWVK5Pxnjj/uIMpNS9ENVqNFCVwbnDFyAawOrnh6XAC4C5NkTQYMAYPMndZtk1kpKC6EwVuljD31rOhz8/cxxHozAOubTKVLcekOAjjoEFLTZzTUm63DygdioI5V05zG/QmMxGViqMCpiZsN8/+lbmyecXE9g5AHtfK1Yf47gOj2ssf3AgXGluBiCuSDqwC+uYgWeUazpzd00+g1hraqebn1ODjrVWdoPO58wiY0FjBXyo4p1xYnFRe9WYHLcZ+X3CisDI36dJixVo/eLok89B0NdEBckh4ji/cDkwx01ivC74Z6GHI6OY51Ie4ZF0wYmeAh4JImz865jQAR7G7wlUs58Hrjnxqmuc8aJHl+wM9eb1Nx9bKXTga8IP9DRYgRcOc3tvXqUSox3jRiOku4YJlgGTS0+zAxtqlqC+Stagr6Er80SHtTsBNuBJ+fWY4nmNB7xJ+0SDjzdt18BJa20CH7tpwfXYRg3UgbHzVfr7OO0xdO3vAmSZdEftTihuNktWpHzbNytqKSDrwjy2YECS2jSkZgzklszaL/UfZkAnTGsy5s6Xi5noOGJkgqtkezBboGjRtu2xIxZiT0uEiC3CBrRWBLDtIKt30WU89hFghVhjIqgLSDJbhvcRwlk3fys+bd+VljSYFIVeyECRliXUwNndAtD2fVew0noKmwP91eflIrjqsU1ZhAbJFlCZtGKCy+vIo459HitA2XuGLrSgMZLuvaCSz4N6zhH3AzzToQizA2Mh0/IaIVyWzrSOyoI4MYmy33AdynyOH0bwvTGcxsn6lHJeaUAv0BoBrADXNduQAYhaVBPtpeUCQBMMr245+GIeHYzV9Oy6Y3Lcr6xvCLIux/DICi8WdsmnMAklr6/hwJyjbkqcY+e1Sbgofeia9KwJ6AZpcjk3F4DS+IrTQ8Qs1HlM6Tf1PzXoktp9HPbK/2w8p7+vtAjgrHzUM9TsFDZBTC3vTL6+i6WxWj4uFVeJFGOiDDC9AOneFa9uWOh2Gg9Ufg4fzaLYSGAOUMK1rHpqGXUEDFG+5Pf3ANPQASYAoddvraH1NmWBTVatOF8O85dyo353nMNvZ/tEg4/jYrlqRagLHzPw8MmqxOjmwDiGCLxvB/ae7TFAcg3wXDtT950o6WShaQnQLSDzkYA5/UOv5ZuaXSD9YiSYOzi96Z3FQT5Ney2ocHTLhN4iAIfX5BDxwhD+wCiFCfNGdqwAWYPDLBfZc4JvMx/b1+9eBwNgaQDb3LFgjF0flbQWiBYF4wAGELFsN7Ng4EA7UqLvZbZWAJqNQVCmQrabpc6UB9uNHDtSF40sl7T3WKtWh1r2vJGlfCJLlE9aCLyPTtsznfnx6RoRCzjzg+0a8SypGSfoMGHu97pGTA7gyIMikzX74T7O18fkCS10AislEBnJEOUA3GD1MIRZhZSI7SmUJbcDDB+exwGRRBaQZTRZHZa67kIb55qqzVlMDogMKx9LG4wELIUGM/DZzhNknXefM+9kOS8apbnd69JorJP+nO4TvXCz7CxBiZOQsg5DKCMVIaAIpyAj7UtK+NDuA3xRgsZ5emegkfM4WzGqsJ9o3OjB+Wfyl4Sofv+YMxxoKm9z0ciPuQAl5RkOf3m/p6clv47+oeM8cTx49+F0RfXcIvQdTFi2U7hvPC2bDCZ6PAkhLB0eABuQqogMTdkFpp5TDp8/a2La6HV0Oa55kEX+XPzIGF5rn2jw8abtGhA4apM160BBhyLE3ho2cq39sj0GPh5rqc0YRTmjNtOzCvm0tLiW4X2dBKPLeVDk6QfrcAaLNOLlM+Qx4pTJhRHAGJ/lp7vwiHsKIkAtt+TM3wBRbYnn8ZFiXodw1OJwxu5aWzARFjAPjMEagyHmN25NFxwrKNr3s41Cw9JtQ7CofeDFvhR48IWmVIRhySCZN7diUOtTzZcKQJjHpIFS85gHf249YyLDA+E4c/EXEWmZZhurGXhIqYbKj1+0XtuZMBps1wDTZn3epYCZPK8ycAcnk7yLEUnm6GQQ1zruf1POuWr6lXyfrgMXFoSGnLcL8AEgbNFuMZR0XbkAni0rsZjgcS9Kgjq3Y2SRuir0pTwfxFP0SzolLgPEpwJ4BYCQuNAgDxWIUSag2KUO0lL0YCLL8GgtwUcBGK5sqdbbAohqxosDj1nIa2x3oozgEB6cDQfmCRDS0lG+k8u5vmZJuogJqnNPtkFgjHzNDEwAcgQYyL1k/chyf4ltAR4HGI8LzAl0lUMDDAFXl2VYG+wZ5WI+r93H+mepucK+jgVhmTIFy+lI4ICzQZrNU7Xc+LyZSJqqacPHBRYIXp5HjqOS8zD9fZjGXxTgQ8oLyAXSejfg5ZrNnEngWqWwQChjBTqZqdx3jYUFFjKj9TaVxVWGou/HPk3982BJEIgsT9s5uiS88LlX62aHUA/ECgAYZcHkcrb/e6LpcgzgeCD9i262DsYHBNNzNUf7YdkkHpsJBQ4iGmgIE3qRmeLm4qhMypDdtOlYjfowLbae5kiH3TbdWdXnYFncNQLdiXYweGyKaVyjMg2WRCBDXTJqi1TgyGb1KGxfU3XrhLkZswh9NgtA6xrgN/YBMGHfGe5P1XO8rLXt72Ij3foSTNuH2v2oRXQcms2F8ojyXTF0zx1X7ZIQJyRfz2cZMjSewAEKqStlZ4aYQszEypR8mkJkeJyFmKk6rw14NoadUYQk4GPoz1+EfAAYBJNUC2ON0i/F0liMwzrTTLIoWCGEaQID2Ge2Gge+lAgwCyEVHJziCrEkx7ASTuaqiIVgbhx4HyuYEGiFWy7XC1Ir/nf7scFN6ASI1qrxSqfNd11th4cWAXEZHxQFBQBRs3DfeeM0SPKj3BQNMb655cDIjjMCdKQJxOkAmGOPCmgpvMvdoAJNUfe+RJ5YEbzV6hFAhzw/JGcqLSoobugccwePzQq++WJ06tR5ozzD2ReP4KXJ3yXkSdKQw8HSRAOGC7HlkIny/7gk1efL9NkCR4ycpDy3NacHallmnYwf2M2FCLwjFBgxCxg1AjpBBjRNN/pjvL9p+i4R0Lr1prdYr0HzFcv5KPnDOUgNC6vkevby8G/YPrHgY2o+eAeLBoAJyc4+PUFNw3KGhMO5qYVWuPcI6CjcpvAGCCTqUlRM4ZQ69YJgMRzOby61xiTugwUnjrdl5oHvxgIciPj6rtdNf7hxX8wabPJ0Z+qscQnOWYwxC5IZq8xI8OFEqucPjG3H2Hfs57NevxF65ylTQqy2h1hGCxfBQULhWskKp82KgRVGBp86wRi+s+vlHIYWXdiYAiWCl3shcg3SXC7s6al6fAxfIYikQZtLip/y/oFLDwBFrIpiOfboWkHMWwoCjc0wS05BLgLNcCFJ03gw9AOAcakwWwekvB/7k8dJOe9aNpQDXJ8L5VkJHFRaDYTg7xRzKDYuWZ2zx/ioCzNjIyDImg4Kt6bsguh5EQYCgDF0p+BjqR4proQAH77QlPbJQa5IsbQgNhzzNe/CZkAwkQ1ScEVwIpnMsrEP61CMvwOQGrxIUzddCHjKq1DOc+zcXYJYQ0AeqsT6/HKhgyOf9XeBKnYQWPFAHReHC9esHxeTUwCxD70L2tz2c1q1Ou8O3qd7uLUzisVf3FfX99St6ZliTqJ3+tuw5wvQQV6hWdCNdn2ip91tW5U9SQk6zpRrKZRXAYoVSMKz5oXPkO/N4rAarNKpyjJp7n4hO0afuUH0emW7aFXsG4QSPDqXClYW9/XnSEASZeAFyOi817f/M8CHtWsm3G9Hyzj3DLjyJleOP2CYR447QMerVyqHBGiq4CgFDlAUsBAWFax4z52i3HTqF6PoaKTrDb+IzJH8edEAHRCB7BzAxDedSn94QhoeA2PfwGOH8NDaHVAhO2jH5umxsP0SmDG23QcC3LvW37D+jC2DJql328oaRXvOcTkG8flxbC6aNPum22YMBzxsWrbOxRg7xhjY9wFd+JbGSA1oXe9TSsdP94Szy1m4H49LgTIzwHolIqtHglkbHTKnQ0oTNNa4GNk94NR3RE2N3gFArSsS0subjWuiT2SQnNOjXa8KGQ/+OyoIBBcq9X72ZQFtTpt+PKBCjVzICCI4VOOpatDExDkRZgb/iQAIm7Y2AOFi4Umx4GvAwU0cVMbIYw4DMBCZGzOXWriVLEai9x57f9Sih8HYnR4EYCvdrUAz6VpYdIdl0QwvF1wXL2Zs+55zVz7FZ8nHAtzK4IHnBjrI57ecYGNqO5pEkDBJD2BwrSJv0IPTkn01fLVI9i0x90xH+mw6nhr/gjxX6v3mnYDr/Zdlma59EXhZPqsrzy1KxuuazpFWSJfg70n3s3tLCuYIFwl8jFIJVLDeARr6+ZDeHNYseOFGip1xpVmF50aQRaKgnicsEInVXgGoK80RkFaZZh21Z/V4JI51I2EdgSggcnH1tGR7vP0fAz7eHngU7ec15/qC0c9IBvPoeYYanbuEZSKpLPCAXfSizLDfF3a+MTCK69hxddFyMhvt8nxNAsIVHlywRltJvWYy/6PZnAKApKXDGZrupzKiCmfdqdXBxxg7eNvMxMtB0ApA4Gk0ugBHWhjIxkK3r05B4GWNLxlMYS4lCNB7HvwtwEZlUnN9BgcvzMmAsjLp0J6LMjWyypcqNI5uibx/WqHyc7XWEJQ5qHk+TcFUiIc8ra9e169TrmWdQ8ACqW4KiR5RnCIT3T/WXrvqbJB9rANMHSyUdZzz+Y8WgfqUNhZS/g4aneN18gpHjYAvtIRYd1eAYD2oundyAmQ+qPQ1alAFakpGnjp9YQmSWQt5tTKHh6XrfXBXHQmFNcfneLZUqNtQb0rzzR9r4k9zBSRPT3DtM+bvrv302E3r/SRGAunQoXpoDhoOPODw+RqvOH6OXhdQPfVOJFwMbiFOWZFBtzI9bF2XyHVn2UdEEhmPjn0zy6s8b7lGTN00PgKvhivQ4OuGpgH+Io4sIKIgpUXXWqbg61bGkAAeJUDXydh75nQd67hMydy9N2qfWPBR3QWPMonSrjO7N2tptn3tTeBkIP73QfBUvzXqT/GFhFJ5cYx/qOtQckJn87YdS7DARV3wrfTRr1EMmjqWpfKn18lI7Q4XvCMCR7ddM1Ps3eMuJpOmADx2jLGjofhB7ZDhW8UfzktGa+6VLFF4WTcjxkACJIx9TG4qfUkMRexR4SXXpRoIVaCN4NWeRbJbQGLZ3VZ8F9w1LAGtmtt9/r1vLhiuFSCzezFSA7YOlH7o31HrpJhzXWdqlFojGYOQMQx7SiW9wlVcvCPeJ82vuAYnt1/5eyr66Cb4krnVwt1yfOryTpXpmln4YpzcSuOUzAos2Jgmav9y7C5ayzRGjqeJMy5BkMWMXYA8IGJFfDjFWHRcRXzslZ4ZUG3R4ixEqIyrTMJtcM5n0rlelq8YttMSVb470huVeSuWlgBBCAXXKmvmHPr5rhzVe+blCa6Pi8i0n89R8B93pfDIrSM/CKPbETu9JZ+/5vqZAryLrKnvcbwJ4OGMe5IBPn6PWz5ip1szIxAQ0XLs69V0RR+bBPPQSbnAI9b3MWLtwXgJy0g3D1HENErvIHSQFagjtvlkgXSXAWX9ecBryzGDgbHA/HIEXm/e3gp8/MRP/AT+3t/7e/hP/+k/4cWLF/jtv/234y/9pb+EX/Nrfk0cc39/jz/1p/4U/vbf/tt4eHjAj/7oj+Kv/tW/is9+9rNv3bmjOezY5HCcf55+xBsQV8zt40Q9ZbH4p3Idj7mYUHBQT3bICU7KAi93uXzGkMG5UKbFJErUzuQpiCJBjHExuFVjql4YhbrCrBLgw+gudpUdY0AsM8WBh5toJ3BlARRyWDCTNcEYbNXy/RhdnAhGdm1jOH8er/UxhoGPRhHMRxFo6QGkVWtm00KA3LRL5873R6ibwrE/q2R0uTNkiM08JXWo0ClFogI4lgEJK4GTQiuf8zdnLDbdOAxZjEcAD+JEc7FIJKbJGeO0o3MBiBE0iexLFQCPsZrjmgia9ZF1+vKBECna+5wK7HZ5XZvlXIHRjj93FS6U4L+uEe+TmdNUBmQQcXR+Go6ZpVZe5EAjnu4a24jnyribKPPhPK3whgo0QsZJfncx0POHiznJODYTMCWu4+K5DmfHrrrAozwz7lvGXuNxrh429yvNb/oMF7z72J9rz6hrWt9nEHHtnhfy4YlnuQAr3gmvlTTRtR+T5yrlzllqOY55vAC205UjLFbfl5GGB7WHuwNXLFL2bJGGCyAyLJ0/QV14zlM17kNvwK6kBcDO9eCRS2JZkeIM0/ub6GgCs2/a3gp8/PN//s/xxS9+ET/wAz+Afd/xZ//sn8Xv/b2/F//xP/5HvPvuuwCAP/kn/yT+0T/6R/i7f/fv4jOf+Qy+9KUv4Q/8gT+Af/Wv/tXb3CraFAj0BOF8u+NBrt3vGE8QuwrqrwlLKnEeUL9IyREpwOp1i+KaFmF38+Bqvf8BJRNgBbPY3Cmilg8HD/Z5quRkt2iwGDsHK2bx2Pdd63O4qSDO8UqjqplKEOcMPMbImh5hpTjUZKnxCMfS5Jq6q/cdQ1N1dwMfTRrEtO9MZ/Ry0bnBm1s+xJ4vYyN80etnvf4oLpj0v3vfNWakMpcMeuZIca4FknyRH8/z4mCF7u23ah0JwUVJY3EPIF18VOkCM8CostnBiG34FsHZlcgOTYCJdq8pCSJzmucMFJJemRiXO5QSqg86aCxotFzP8QoExe8499YYeFgU6CJP/Mp53hWKtXrxnEWw1fOl/hZdPvCC+qi+vl1smfSY9IzrvXu0z63GldSfMOPSShi6bkwcXilUdWwpsO1KgnBdzt2p43T47XCta30+Xguihduc674OeDzFZ69ZRo6f9d9WtP7Z4XcJRCQHpfBBBb0tKpeKKa1Of4ys++QFCLMPGQxdhT1RlrNvdu8KPtSSqdYosjVEIFCzzL/WrNhiPlCAbOczXuCyAhAHkh9T9r4V+PjH//gfT3//rb/1t/DLf/kvx9e//nX8zt/5O/H+++/jb/7Nv4mf/umfxu/+3b8bAPBTP/VT+LW/9tfiX//rf40f+qEf+lidfKpVU/HcZFq4TxHhNaaSx7V6kXmgud7fs18KwaEEs0pBu8aIWvgAZ1BT8Mv8rKWvSdsm5IgueKl/EPcBeuCop3YwBxDRtGNRM14BBg22PwgLeN/VZTIs/TRUNEygJ2JDTACPkZaWiLVwwGS1LsTAhxbfshoFxoyHgQ2fIzIlAWLptWFp8eJpXmrYJK4QBJYd4vu2xP4bVmuEh1k2xNLQ9GEUdIwAJ75bp/bfR9w0hNB2LHnQNTOfB9GxCNZvqz3T9HKrd/2noVkuTBaVImNMTTfDg1tpJJmTyW2AAsT5gM89doDDILGNrJC8Mhn7TJ5X0+lcgFYNUPy5C43YOkiPmJ4xdNN2uMbl6863rwdgZmvtgAtrOsyCZ5Id+xYEV5pqi/a1M1b/iHpOYf7NNfzynDGOOcAE7zcBnJk70/3jZc8hblVJd1JVOKKf9pcgxwaQTAG2cY66IVWYXo6OAlq7gcDcuwTQSHojK1EwAazDdR1gFfGntOlZN5RHUz0/sok4+lqVjdnSW8DOyNT65IXHQw/fwZTGi/mQJ//UsTH6NZlr/5TnLmutwDuvHE1uzqAiC4J3UPC2ABJWTM9dX7M+kE+g06KAhVtaUj1FtyHXi7rDSHlka2jCc6ruIfOpWtASQDkYlQROPkZX3MuPtV9QzMf7778PAPje7/1eAMDXv/51bNuGH/mRH4ljvv/7vx+/6lf9KvzMz/zMVfDx8PCAh4eH+PuDDz6Yfn/K8pF4/RKEVOJ+nfsGRdO7BCDHKS93r/c0JiXOXG0lBxmWRen98hpfj3QJlZwVrWZT2k9hUdEwYATsHMGrMrqWGFvNs2YODN2kjQ18iLknJhAgomXOzQJQ0FYdxeBCUiwFXtq+bhDnC6oVjTJcLF7Bz8ct4i3U3+jyYLYGBRqDcDMF2JgDA16tNEEExzgpuNjju8at0BMfLB9KF7Pn3c2xzgydMl1q20IVscwZN/engzctH/pwZMAk2OS6xhpoVg4+KxAmVYjTjgNBiX9SqFKCDwcA+lesJgMc+evFe8XKxdJyWditCBmnUaPDWu6IrWKJX6+V8v0x92L8XqpAKlkKfoY/+6GfIWQBuKGF5ADKJktUXidWF0EzCtx/fxBSU198LXS9b8c8X7OVwy9fn81SwKXwufhXP0XiuCRv8ZL+Oewyz8mhv8FbG8X5FOvTNzJDxDhdawLPxJrHKvgdXefNlTau9e9CKfRjBDEH186r3x0tKmGZeJN2IATxDELKPutPTqMuC1IIO19xl5NEDwRiK7yCD393XuOl12tr5Rnc4kFWXbA1y14xOpk3+RPbcgNA4yhchmaWsmI5TJlbwMeFkivxncScvFn72OCDmfEn/sSfwO/4Hb8Dv/7X/3oAwDe+8Q2cTid8z/d8z3TsZz/7WXzjG9+4ep2f+ImfwI//+I+/9n6vM8NdmMqcBpAEfHzP8yc6OrQk8MsbI2j4SPyRnuVoGZVQ6+Wv9ynS/ORK55wbTb5vmQSyAwQR0ZQtzkyHKClt4EODSDeNn9h2E7iadtooXUtRYCmd1yU2wIOeJACDC+1920J4+4D7ghIp6c0iqJVEfYgPAzbNx0wXtrBL2rAYQ2SeXSdiJdNd08hAVBszk2I17mNiZiN9/lLu5b8fhVl8P6q5/5CNAImYDg8sdYA6KvCw+aYad3JoUeUgapQkMztIu1krJz/fUoi9P8FUSwuN+fDuzy8KAKnQpv8OcfBchU9atiqL8/6pZcRjYS6fWS0Pc7/180wjRMBilqW4hzNWx89+tKSwCIJxC5SvvRyOGMBwNUFUu8RMuQ6iW5zu93BBpu8EAMMpDUlz5sYbwyx6nLs4D3eT7nP9lUqj2l8DGO6m1IHXfh5+89TNtISUMfVA3pDKlzEj073rOrnCl68Bidqib2GhpIvnewyMVMXjuqWbLn5zcDqKK1lp+c3AR/TFaCfjnNza6EqVR1kobSmrUAs5Ya5OGjZWkgQonGAhsTulNaM15XtlrJsDTSLIwkC3ApQBgkgTGhqZZURhT/TFjo33t2gfG3x88YtfxH/4D/8B//Jf/suPewkAwI/92I/hK1/5Svz9wQcf4POf/zyAxwniGmFV7TmJ/PoxT1lCfAHNgKRE+kY/BK5eVoKPF/z82Rh3BBJ+3PF+rpklsCjMyf+OFNgEHzWYNM4dKgAiO8EFcM1Y2UdxvxhQYAKoxFuExsHzA+iv9jYHWaYFhCMoikTMJFheEZyZplTXxaveQ+KCuWokBjh8IQePYwMe2Yccn1FoIrN/6nwCM9M+WtLqnLvwFJvQaZadzkQQgb0H2gvx4r8dwawxCYcOQlC3XztYP0IAlmewd6rf03w4lS8COMGFkQVY0nTqpCHFNezd4cKkJ9VuaTGL/MnHLDuUZz4mi+jy5yL/nzjHArQLg/b6LgE+fArqeEodA8otCKbxyJsHtrqmOPnYFVbltK1ptE4L9nt9VgdEBlBE3HJkQc6w+KoLED4Hb1d+Wnls3RH1gl9KAcE217neTPDawPpoxF8+jHXQKr1foXua3vUCPizJv4/jK/PlKt2h1JKRQrcHTnO8vwtsEaDkrB6uKcmfaebZqH2xgNCs86HEGC5lyrFzunO69OeusIdQYXQZQh9roYkFsRYoSX5C6nZhtvvYem5kFmSNRMmLOv+zTk2j9jRunNrHAh9f+tKX8A//4T/Ev/gX/wK/8lf+yvj+c5/7HM7nM771rW9N1o9vfvOb+NznPnf1Wjc3N7i5uXn0Xk9ZPI5tFiBIje2R6z6NkuNTAhsoWj3AiSBKLuc9WuOecvMzoCxRP9wNHn6xeCEKeom7DvbiQjAAEsJ1WByEWVCIs6ZCPEsJOB37rp9NewKzFXSU0tcc5/jOAcmV5x2iQIbY93aRHCBjOrGEHxHygJqrY87KOPpCZGbsYwsA4ucfgdhMHx4X0uvUlHlN5nvUGPUIznnxc6Q+n/7GcT8X+mkdqHebsFT0J59Xhm1I1xpa7+oa6l6iWwVB8zrVocWmsARMsJXtBVorNg/KexKoWBuyBYs2gVyF1LROK74RhCbnJa8jtrSZbm8D4LVTJm5m3DvnIFdfsuigTAhsE0DyI1oKxRh3mc70lOxquQCAGu4VXSlMXBBGicAdU9fdLSbN6uJIOUYuTnCLbcRIAMrze5Gfdl8HHoCElax1890LoTUBt2NtGwENG58CQtwdWvsR82q0FTFHPS0huhu0xqrk+BCoq7XKaTAggnhMFiw+pXDSupQO7ahrkN+n5uwaIyEb8+ZkJQacy7u4RSj+QQjoqPwZ85qUMobzzXLDuD9Pf4eC6J+Bkn3UirvMeKDdJ7b/IITLtbduY26guSWY840NW1NXdiOaCufpM0gCRosJEWa1YlSLlgho9AgubbbvD3XbgTv4sK4g5T3lRocheV17K/AhIvjyl7+Mv//3/z7+2T/7Z/i//q//a/r9t/yW34J1XfG1r30NX/jCFwAAP/uzP4v//J//M374h3/4bW41tde5XLxvtZ8qZJ9+lqNJ3K5Yj5pBCi7HtoKdidCeaFQWpAIPQmoEyV2q1cOgMtzS4ZYEtzB46eewMkRGS4n18HtIEaqe7WJgpQIeBSGXzyNl1QbD5LKg7ZO7d+rITmNZBbX9XWuZuAYt03hdjqUGkloV1AI+jtpeCK+4rfWRZ9CSx6bVJOJBjB6GpGvheE4UXpPD+yPN5PKjTQVky71ShNGkgyT3B1FgggAe5EFizQWcpzD7fpeeApr8g5DAmuO99jModOp71RxdpJiChzpzUq5Q515Ewg1I1JQJFuGhyVNG81XYg+CxMRU3uHBWEcjz2Bbwodcp6d42dv6MDnXq36ArrhngSpJNplAGxft+QFSBkx8NOH7Vf0i1YRAaevSDXfCx6O8N6EIWSNhtbNUawYd6PiIMHr1YPHN91DUzxkBYBMqmh9S0L2ptc529xYMLRE30LACp6GyxG3eM1OW65Iw1wWG9xF5Pbm2urSUvccLgadaMUOrao1nxLIQRZ877BflcUtZnCv73NPjwfg13gRfwIbCtK6UoYEQQ6gV86LsHuSsA1D1tfH4k6NEzZdLumDTuU6QB8B5T5eXpyauVGk3F0jSAAhEQi1nlpABKCcYVT/6dAh9f/OIX8dM//dP4B//gH+C9996LOI7PfOYzePHiBT7zmc/gj/2xP4avfOUr+N7v/V58+tOfxpe//GX88A//8Hck0+XYrlk+6m+1XQcewJHsJoGEwnAOGq2ztKeETA0YnBQxf3ftqIKO4lIJpuFMpaTJRoZJ+HtHAR8w5pQ3DAYQ1/CX3o8K8/LMhGmUHNVz6UuMRSQTX4DDq+Zfe59iK6yvuYAojjmOaezDkiznYn702Ms5meNA5rlzRjyl+pq25dpiagPlOUsRtTon1y0os5B7rDUo3fTe0bpaP3IzKaOt6p837aWvhfmrbIj9SJqVBtDKiFkB0RmVe7UrjU8quD657uNiR4wyVt5vByIBDCVpzGlhG8P2pGHbnbjEK+wShekuxqV1tDanlM4rlhy9+aSqOBPbYwiS3hKzIjmYYECtED5HBDTqlproGCFBnAuMrG+jB7FrtRFhbgKiWnk442HC6gkFkW75U9Drw6dj2Sb6pkKHuheJn+eFBUdJl9dtEMZE48yMbdumMXPtt7WGNoZ+7t02DcwYAYYArEWt2H5rUpQtWxtxP49rA1kNHlOkyjoxcTnFO0RjiZRVd195jF2FOxMotWuJxN7GqJtwOt8ZKHzQriHomK9cmfchJiyOEJzP56KEKWDWWxGGSLkS6fYRNuZueeiUY98bxVYLGaCaxf2aWUVamTsA6KXaqcbxMKa679B0W7fAeByJX0MBoLsrJejzKC/fpr0V+Phrf+2vAQB+1+/6XdP3P/VTP4U/8kf+CADgL//lv4zWGr7whS9MRcbetoWQLgsK0Im9hlyP1pEKlKtp+CJYCrmY89hjPMiMJPUzzSmHUo40infkm+/6eWn1+hRAKVMTlVkghJctWmMYDlA8hTZcLFIqeI7dgkNZq44CEajKrGI6Ykb8PgVEeHptFseKwYznTFZffM4RwITpvMn8W4V9Fcj1GH++eu8QjAj3gdh8+TNdboaVSL5O1HUQRNNRYfUoNUDYNKm6B8lklzGQiPqspnVqv00rLXEmwztogWHuTw/92KwbzkjJmFG6XWy8PRe/5YuWnszf6d8+h1m1+T6puT6cOU5WgwNDk7qPi//We5r0DaQEeLX55qBtGx9qIFrQJK1nDuRjb5EJ2CSIZ5Go8mgkBLeAeUr75AI18LE0QidKk/Ri5ucgMoJ0AqhFkKVmBKjgdarxsVRgoiDOfeZVINoV8zzXGH2pJGqe9u+Bgw372Wv6VToPHlObQHc3deFm2xiMZYEwY9lH8JMtKhYPtNGyOJ7zQSL0pQf4RTOg6/y5NXMV+fwxBus+SK1xCsN4LrOIirt8LTYFGRQfgL9s35ALwN6N3tUtMEU0lXXTpo0sAaMpFxC6v4EBY0OUQfNpCXRwMr/SXdVoBh6x6aNoej5zBtOzBbsHjcJTbgGhFs/geMstnr139N6wtKRJ3zF67d2OU7Cij06AaOyGA6xGumbY5sQtWmHhEAFZ0Gm3tSwASBiDLUC12d5ERhtEyfPHvh8p8dH21m6X17Xb21t89atfxVe/+tW3ufRFq0DhEixIgBA/tp6nB1VBdXncfD1cCKj09QF1ZR8hSerm1QxvqVN+XRETOMZEnSAOF6/agVX3CqbBg7GfN621IQV9ikQFUs8wUaG3T+CBlOpD4IZgxAwQHMQk8OEAT9MYxrxIvHw/CQWNJZHyIOgrAKlWh9DRZX4WTx10hqIaF1LVRDK1yz1cZOrHsT/7vpeI/h5Awn8/ZsLY3QLYACUbw+8V9ShyXvy3ZVkCpLkGOGyM1ZpRxtfT5YxptWD6PgZmAvc0yBKR7jn7zcFHXQNkurf5f3vX5+5lvQV+qHn/fh0DMNNvTsMlG6cCzEp/aBw1ZXwvFO1WLy4wH10V2Sy5kR5JKxsA6nz4GvN1xqKF51gMkAcNa3LjunSsZsamBnRaFIQ56KOWwGMxK1NXgdsc9Nnz6jVaClmy+XPeEJjQit0BYdp3iVn5SqVdF1z5m3GaQo98HG8YaXKz9ZtKxVhGpNiPfcfYB1rfbAPFXQMPy7r0+V3WFa01pV97vgCz3RM/R6w/5mHA2sFHWnvD6mHr67xtuo7I4kcEufb3mqWmv1WQ3VpDW7rFPiQwIZsbTYhpXnnHxtz5lruPkoeDku7qMQFqpVpLR5wXIJ0S8Or6TgA5LCtpH6koKbUT3Mo4POYP7g6UAHhL7/paWsTgtKbu2LEwemtYeoOYcjuDLkAYumEhke1YThqT1xrQVEkNPttyrevD23xXN5yDO6/VhMv4oafaJ3Zvl2rGvqo9i0R6nDc3p4kxs6M6cLSaVMF0DeAEMr7yux6U2k0h32C49of+wF5OBsBg3Q3RmweD+qKPQl4C7CWuY2jtjtCmXXhZrY4IIBUGOKuAOkFNsQgI7h59j+B9I7h44sP41zHyhbjvac5tvceunXWsw8pwAB0xZzAf7zXw4AKz0IWPKwtjcO58e0ybPfZhZu5az4RaQ28coKn+fkzNExFsu6YkB0gKEzLUTEpqHg3/K6W2DijQWNc1aCY0uNbQi1nbtcvF/l7C3VIsH93dL5qr35eu5/eG5bSaRWWe8xxThPbk4COkpb+HQE7BmoRzWBdm9YmdUWv5/YhLUqbMBVz6etvNXYhCq3xSa9/Ycl4bUVmTLugNjJjWOSy9eg9Ln452g2DtTa0fNnbNAEbrXTW/riZwag209KiD0JeOtvSwdnhxJy8+Rvadjy1V65EAAl/PR4scAmR6qrjzgjGBDzJhVpQPeyEUBY0TGBtb5KUJMFGBLCyQPtB6Q+8M6g3MjNVcMMuyaGVfHiZ+CcvNim40q7s6kwlqVQCEgAVZwG92Zdr8iNMDbA8SNfNT766uGc/SGBZmLmmpZcwk780Q0AA6KcDxWCUCQEzYzY2nWybY+rQ1FbzOASARapaKx8o1o98BhGLhMjnm5XIlhHKkNC3YHXyYwsHscR/AsGDs4S4gQOnX+0LQ9Wy8VfmBWiB6I9ysC5alY10WrH0xXmFWkaZrvHvKrhFJI8GwGB0CwBhq+YBE8UcHGJlz77vlajwIaUnbNNO8RfvEgg9gBgf+9/TCdVBA5PB1BhkVsR2BR/zlikP5gnB5fBwbQnr+zlFymk6LMEeh3NBafIE5+DCGbBVHXbvJ4xVouFtA3TEjYy9Ydw1w1F0FQjzj5YCXd0nQo3/ZqfN71PIw8HG0EtTxn8BHVDr1Z/dSyZ5Z5P3061yCQz3GwEe57zXwIXat9G2XZyQL4hLWxWRDUNNzbeJ1kYpgD7C3xxiQM7DWgd7RrdshGovlwLcDj8VNatJ3ARgAxDQcBx/dCoz5edQMtPSGtpppdlnQ11WFy7oABHWLTaA758cFefdYJL92gBAUSxeuVxH1WfKsq5hbBxGImCLXfEODrBa4bQvN3AFIa93eR8xLMEa4kM+spQCV0m1ecs7VBSRYujLjvlgZ62UJLVrBR0uLxwQ+FvQKPopr2PsTNUNiUAz8GZ2rJULymes6EQcfYsqIZEoNYmlGGq2Pt6q1ltljFX1hextptkhDKCHkmjqBrLIsM2MYwBSYKX4kv+zLokJvXQP8Og6AXc+Ff42jin6K9c3jyCAAa+xB69m39EsVK9tRMUQCNxGNe9EtUYpcgP5N7ADaa2qkAlBrwZjDLfB06IaR+owsSEeZXlw6VawfBfDZV4yZH6klz11sOsUKmC3WSoDz2M06YopZQyh2SzNlZFU3jECwSlpSuqhNR/e5aujsWIjieoxUMondxsMgVquJDK2iTOAYQ0Njwbs9ADXYyYVQebx9osFHbbUipbennvMxy0d91+MICD9bouZ6rhRp7UQKIH3bRTuMc4YX8hqJqpGCCEQQDwhjY0oROCq2f4mnxhmDscwV2RnCA7ydI2jM63O4YGmxmEiLfI3dmFQA/cPwpKbvC2TfdzOjqUYXWSeFF4Rv0zczIwFjzLVAbOySaUiO6dQLAdMMPMqIqqZjGpCDszTvJuC4CCAtgQueMSHGSNQc7EGket626XM/PDyEOToyLUzjo26CWASeb3FaVDu8ublBb6qFGG8Ogam+c9Wg+7KEhSK0bhOAy7Lou2vfRjNhCiWNU6DesN6c0JcFN7c3WG9OWG9usJ5WUCe8ur+3OUr3WdZE0cl0y0eNserLaQIeDqJcOQ2sfVQQOLV213TDsmWCMsBhMak7UDmfz+pi3Lb5PNaiWdUqKCJYTMtr8JgNxHOOsIoZrYQQZzSoBaQvFACj9Y6+qvsFvaGtNvbroi6trlYP9XfnfLhZPOYIBjRDpS7UbimmbOCjArR4sT/3QGNMbhcHdRQBmgA5wGJGlEzdB6KmjI8hoKDK7kFjgEfDQhpcTkRgo8V97GgGqkGE07qiLQvW2xuQgWPv1SAVSH3tQWvHNSisPHGMHf2sFpZxtnU1JPoJy2zywFhBAzcGUc8YLGNiYoHWbTEh27QkuRjfqBapQhiW4u3k77Ttq0FdPwoAKcAeBOriJAFEeWIN+jtmOxFlnEtrPX6U8MVTgHQWYLf53Ie6qoYIzmPDYHUfOq34Myn4ICyr8ogXt7dYlo7TacXNumFpDad1QW8NNyxYesMqDb2Zh4BFjWImKImg1nKyQPQmkMXGrwSwtw6QWJwHAyIE6YIeVQ3xxu0TCz4y8E3i7yOje1z/wgwejucVKnEhdHnFg0biv7vwLAurHZBfRKszB8DwtDlyjcMWm1spIprf3CZSAk7juCHpbjm8hB18ZO/9aaLAV4Ave4bylEergVsxfLdYe9Bp/DQ2gEPbKLubX4x5/a66UiDzovXrCuSidLVM2kPGjKiAuQQfeeI838kQFbTA/Z/2/WYVWceolU2taqNbLHo8aJShX5ZV/a7Lgt4UXHiwZTMw1g1MdMvScFO+ulsWUG8JTEzQOTiIwTWtu68LWm9Yb26wrAtuXrzAcnNSAHJaFVyNHTJGViyts26CUq/fDuBjjcweKXN/MaRSwDkQdEsGlmfwYTQ4us5fayo4C1DpIup/LnNFQ4WqyG7rCAakBc008hY7Aqt3Xy+jtNGKtSu0bmEQWAFcI7RlSUuRCWBadT7cAoKu8R6IYDsg6qZE/ZSyxoOgJYGbg99mioQrFvXlGWeDgCZonGPc/BmaZPSpaepi94IQqIu6M0zhyEhOF34S86S1QWAmd4Aao1ltktgGwUFy92DnninaTYDW0E8LND5nxFgHz2EFOH1vaGRZNmRZLh7o4PRicRLEDHZwBeheM6Tqnzj4aLpemtGv3Q3uop2LoSnwU8tH8rDqkglOwxRAXRi295FmDXJLK2e6lVJO5VVs7Zvbgo0mqZkFzlmIzY3lYcUKZairZwARvKpzplkyjQkD6lak3rEIQ7utbkWBKEihBkECZvFiYrm6C/jSezWPy4lfdBzZ1h+z0iCxus4YjGsy+qn2iQUf3qbgFijzDkBix1wVdJPW/JomVyTgxSFixHhlgCs4GQwIR8VQ8QCcZjsiUgMVjQQVTDBb4bAEH5AUpLqrrC5cEcbYzral/IgMAm9si4pAmTETz5ACeNiCdNBR38N8ao82ad32Ynv+uLMFHD61wdBEpMVEefyNzF99nAc3W4bp/qBpTUFP3nkflwJM9n3Hy5d3k9nc7++3bW1B7ytWC7jrqwr1QRafToJOGrC59IbeCGs/hY/VYu0BeEyLB4MCvS8ZR9Aa+ulG/bprR+uLvUzTWRbjzxyCfzmpxeP2U+9gPa148e476OuK5bSiLV0f/eEebn5xM3Ck5HrdgG7ggzIzRt01M9jJCZyw/bQeIg3cgXCZF7d8NLPSjX0k+DCh07qtoWWJ62C32BqCxj15HIMAy82N+sGppBnDYz/0vlsB1A48tLy+xj20RminNYGGxX3Qmm4Xj62ZMoqQ6wFAbBlQYMcM3A1Uw4CYOEjz8TEA4JagxvYMJesr1u6wNbAPEKtA8bUEUbfJYgUcpAAaT8+nwbErawj3yWJghaUCjC4llijTbDXWqIGWhuXFqlYd5JxH+XimmPP9rIGuu7nYxjYyGH4weAiWRTNx+qK8cd/2DJQ1Ec0dtiYNgLo7EpjKwh/jxHyjSudpF8oooDTqc+GAb1eh23s3jOc8TswKlvMtRAGg+9o0Jbo10GAMUWE91NGFxoLBVjLAXDoMgKmBG7CL7uA7ZCT9aCkW9KHKw5lZLR/bgtt1wdK7xoH0jp0Zp3XBDU4YAizN4BkROsHi0yQWdYfKDybSWkIGDKO+CxN2H+NlhhDVSve69okFH5PZ3No1ATUxvnLOcd+FN7qXM2e9if2YxyjfSksEkUf5+j05M0QGRwwGANAApJFXLIebpkUEvO9KXGNkmfO4z4g+jG2D2BbyImybwemC1n65VxNwzasBJghyEdUXIws8aeaFai3Ddq5lFstIKOPlGh4QvuP41499zdjnPElozzWgFGVc/XP0u2ruFjDl6kLMU9AHEhxJ0TxFmRyV/1rzdDXzk/YVy9KxLCtubm6wLAuWm1OAD2OB6r9nSdcWpwKr0egN3YK/SHw/FgG1rmC0qbBr4VdX4KEBjwrCltMJQojMGAE0rmPpaOsKWhZIU5/7xgOyWUR9sxLJLZljC+uLaf3dgyVNyJqFJ8HHrNGFC+YwRwBAXZkjO7CewIelVLqLsA2NnmeOdPGFdF20JWvacFNBRMxQm/AI83hbbJwO4AOgtLpYzIjHUkCU9YuMDDg18NEMfHjMh6cro5iffWsAxSCzSywA3mT5KIDF6bfwEYl1kBq/u1QUaKTLjGMsVTvl1jFYrTujXtPHStyaYG5dDANBZT6Za28xdn3WxZ65kWW7WLxBxCUZaG6nBbQQ2trBJGil+m9wBnOntK4ZNW0bmlkzBghZFkD1/FReOhtLMd4rjRWgwTTv3iIepVu2WAUdnskVSgVkBh+F5wjSUkddM4VGAcaqpCpdesZZgNrAoaacwZVmtfjomk3aKYwtgLLPHduqGqLAYxNBjkwsQQD6e2OBbDsWYc2oEcHa1XWzdkZrXZVMszRxF4iYpajBUm4dH8+yVQyYopHFz9gz2tYIk6JHpOECb9g+seDDhWHNEDi2oyC9ZvKqrf5eP3uEMQzpc5hOUTRnL2YlkwsDljdNZuqNFEovV+4+cDP3sVWtEU4thHnP2IL4zgMys8bEOG9RUl2sqqffD5JxFpNlSDT7gq6Ml/tPIx1RBHvZgG1yqcRwSDDCGOTCeH1s28XoX85djGHt7+EZYmbkSiwHMsPJzaqxj3jRfL2C4rXnJ3iKZMPSNH7gdDpZ7MYL3Nzc4Pb2Fi9evMDpdML64hZoau4cGGAZON9v2Lcdd68+wr7t2O83EzACaYTWF6ynxdKrnQl6EJcFkLaOvq7KSE9m9Whdqwo2wnJ7qyPBSQ9tXVT4riuwdHBTSxTvuzIhCLhrBQ9Y6e0oVNZKul6HAekWaZQHg1EwTDq8CzC5YMjmsxUhGJUrSwo5MwOLxRQwAwagl2WJmB4eA7wP7K0BDj6GVUEFacEvD8o1IdnINd0W9LJO864EwjIAMKgTqBP6emn5iE22VgNlXg/B66oYnUdgpNGju9omJcYtJ5EunHQc9OwBtiIILFI0bynjyuambQtr7IZZkKSO88LJT7ziMaBWkmqd4cVitmzemz2X0UtrDcvpZGPVo8iY0mvH+uIEdAI6wMRWCTPvQGYq4yFYRkfrO8YytF7KGNiEMshYSHlZ14JeGihJJtjMagON1xkkARj7smicVOnzROMlE/KYDprWzuQxzV3O7n5ggdh+ULG5Y+8YY2ja9wQ+VHlJuaRrQogv1lVwuQIsPRh1F2BnYAcwIJW1zf0XzahZeGAbAzsLlt5xOxirWe1OPJQfrGLW7gWdGdIXdLNsagGzpEcFWS3cL66YVPARz+Ey6jtV5+O72SKDwAjDAcjRRBamscNvT7U4jnLyVZBawS/CBGbSbInQWLRoi2u/qtGQiMYJmJlQI/kHIKoHsTFHRfmwxcYYYyvukxE7zaq/0RaKIIoCZb2OXS0TbOeJ55VXC5BgM72mBu0GwjarQ9QgcbeLg4AYtEuidw25ahoRvBhFvKsVolgyfGHW3+TymFoPwzNqlNHmcWr9yVTb2eI1A6N6bSLCu++u8HiKbhr0uqrF4/b2BdZVrR43p1uN5egaE7CeulW/ZNze6Pjf3JywbzseXt2ZK43VJdC6ukJ8/kXTQL2C4GrBfKfTLZZ1wen2FBkFDKj5qmudSw0S02fop9ViQzQ+BH0pYE3n9XRziqAxD1bz2gBRoyKAhDIXseN8zo9ZB/5vzRaoJEGSlg8wh8B0038zGsZOtpY0VU+r8TrzN3A+VEPe9x3dxpR3Do21ecAp9Qg6dNtDd81U6poQ9bEzgWWAlobWCa3Gd/RuwlQtHrACZGgW89A9uLVYOjzVNu7uWnUCNCHK4mKuUaLENjnoKLUh0mKDBLSMdDcOsxy5tdWf1QI6QbCgATUhcF3HBqDUhaSuAbdseDbVslra5mlR0NdbjMNyWjXW6PYW1Amjm7UtiphIjAeYwI3BzXmsZrgMAvZudS4cewARLDxtz2A0K/Gf+R5aBkZ68HbEVvVFhT8RcvNCQuH8qUhF2do8LsA4ZaxIsxgVITZQQxr3AliclCUOU8auEdgCNY06DCDD5IdWavUS7qpEbMI4C6vlQ7zHlKYglLn0P5gh24Y21EW/DC3NfxoLdmbsg7H2jrFqfNqN1QfprWE1q56wWB0dmLWxpdVDinX0wKsBTQ9+0/aJBR9pzpo16qeOr41eczyQKDLOFIXrujgoBCLq78gTPHLdg0dhkfgBJNhjPigIT0gDmMDIDeI8vsI0vWm3WZvMepwDoMj4GFrcq1pMPPWzEkj9zXTvMhZitDvAggAfaXI/jKXPiYEN6hRBXy5kXRN260kFRZ79YUgv+8AzQQskgMe2bQlKI9+eYmyOIOcYy+HfWa/QWsfNzW35rQX46H3Bzc0N1nXF6XRSraovodX0ZTHGByzdNTLNLGoCDajb9xiLsDaYpQxMRnsqQJe+YFkXLOuKdT1FGu2AGANURuXVB0EI0NHMTZC59iqAiATraTXzagsmHAGsLjjLOgmFxuJBfP7r8b4EZjASk6bfMQNN/cNpgfJsDIAag9BBzMHo1OStdCNNTexoTd0K0OBS3TXZfOUOoFrGqyBqbCRdNdP4PVUS5K4IAS0uTFOjh1s9FrNy9K5A0VwwzQJOY+wo46sSeMDFrprefS15NkYdtLJOCIB7Wt06ItLiNwcf4KH++MYRN8Mtg33ZLAVVsXLLhuKfFOreYfIAYB/XWkbdN5azwNsaGL2smlmFpjEJraR1kz9frEFBWwQABR8K12mAYPtP0uoaAq+Mr/8RtDmB7FIF2CfBgFkrmTpwuiwTIYI5eN770BSgueVDPCBbBPA9glxJts3jfOxnXtQAuKVJb1TlkIudAY3J8xiXzDYkWFZDnMNQN43XgXLL0xBB91gNQ8LcFzRA0/ehm1VK72i2V5TGRSltHXlEA0xRaAoyq4KOXyRFxrxd05qvgYqjkIFg1qqfaASKnSHFEHuk9LqtK6LE8/4jwEABHhbnMfbcvCmRtWFiMb+0gYExUqPnXQOrPOZCbEdYCNTNompPgAg2v6SWU0/LR91OW7WkoeY2By04uKpscceCpxwbXdgZxDVpwS1rVDSy0r5IDT/Gv2jDYgyuRnBHAOnYU/OKoddn3LYtAIj2TQsGuek7g806CM38+S3TMVtDxDVY44LAiDoadYv1WLD0E3pb0GgxvDd091wS7GcV/stpxc16wrqe8O5ygjDjxe072Lcd2/kcKdDOgFvruaeIBSGup5Oar2/MumLgQzVSB4pi86CmburN/PAN5M/Xu2ryS8dN76BOePHOO6BGEQQYY25XLosAgCr8AFlgbWH4NDPISXY6aAfSReCmac/ckAw45e5CrkUxqdYWXV4et+AxACxYqaEtKyCkQaqUaePSGoQamJwOiou2AOTyp4KiITAOrMLUYhraKbNdAnxYzAdMs24taai+q8UPCUJk7spQHVef2S0USMuHx6kRIbT0DEjV1eBFxtzF0rzWj5S0fhGMrnEUoxGYBuAxXHCr6UCU/Y97aLZEWxe4NdPpKlxbDkiWrqB81WBsdV11uOVDrM8xBhZwqiC8Y3S1Guxbx/agpnoWCWXmmrJD3YSv6DiyAaa2LGkBNDBKpfx3rHWn10LHagHObJK98lTWuAlY2fhGBBgfjP5aVozXB0prudICI4/v0kyxNdehARZXzExniNoeLIJBwDDLSFhSCBAPJjYlTkgVh0HqqlEQMrAMBnhg2zr2bcO2rFh7x76fNBh1PaG3jlPv2HvD2hrWrpaQhYcpQWndbmKWpUZRjVZagpD9F4Pb5dhEJjoCUBDvpLnGGQgYe1AzlPkWv7ZrJKp++FpEaA0BHGbgI8YkPT1WLR5qhfCS51HYK4Ii1V8JwBh1KRYWFo9RLBkjmPcYXlUzGYj6fmudjwQdIWRKrIqXnp6AQZghHVn7+NrYNATx+ZjHmBYBdVzUYeZ2EFT74QDKmB9zft+QQMfBTxWCMHO2gxwPnky6sHLhpQpgs6I8jVxr9YAwZ+wGWqhZ6mwrKa5eTwTYZMcgjU7vIpCm+yqw77kAs4rY4h+iGol4iSMi207CtXaaMggczPmrBgJSaN5p8fDjfYOtZsy4rZpJc7q5VQuKjCxiVAq8xYxRDJ7e2jRdmcZ9Wka+mlRQxhQ5bXNqhEb3Xj3SrRbi64HI9gYBZFAcS/a9Z960ZQdA4N1oa1TtMWnv6ucgZmg/WlMTck9hRUvPmI8D+CCiACW+mZprof6u0TVh7witX392UGGCjDOItLpdppXkKriU3+J6ZIGXasmCWw5cuxdRV5zxMA2WTauZrUykbSG16Fh7Rp+Tu85rnRwyX6gT0K24GRBrniR5Cpuk7ey82QBUb2BuoJGp3kr+hEhTnkiUtEYLITe8u7DQEDwI+aKRkSLKmF1r1dLg57m8qZ/LIZOsEfs7goCSj8Uhzhsh4WZS8MExP6oMVqacc3VUCLj83VRz01RcIPYeggC9Kz9bmlqMte6NpmY371jX+CUt025QqindNZtD8Z33Wj7Pm7ZPLPio2oU2Q9K+hTY5IzfzkADddrcUSyITY4piC/A4Lj5QXs8gNHCY5mWTl1uDe3ii3h+8gXfOHWTLDpE83CJS/LBS4hwEuYlbAR5sAGPfN12cXvcDGTQW2S7MAT6O8Q6RORXBcWp+bOWYWHzTceUrR7xlUykdhxbI3AFYWCPIj8hFPbl7OMFWDfBisw6AxdwePQLIlmXRuVlXtNbRTWtqjbBYLYzFUjNtRo1GqIAmBQbho5eMIXI57MevFkjXui7nfTDODzv2MXC33UEaYXn3BRrvWGQHCWMfG05Ni4oJazaF3kB9bB4Rz7RERo2P63Jag3kCgJfKBgjSlHFFHYqbU1QxRVMAvUOFmwCgptk56+0tlnXFO5/+NKg1DWjmgX0MjTHyzKwyYgHkTdACmFJsE9cbQxTL+ZfMnWhW/6QWGYMfa3QfhfP6CEuYC9uxm8a+m1sGWmOi7+quHNsOEY2qFxqlXo4BkYn7UfZaRLVFB3VLQyNGP6ng7DcnFaqnNV0wVsmU/L23ENyTxcPGroEytRSYLR92a3brg/EXgbtHvZsC6QmujsoObAt2txA1B+021mKKjixadntsuwa/2/jsuxZvq8FDEX9KALruDxLPF4XvLBPr5BlWHeuNWj7aSa1stKrTxS0FIqKVg+G4UzAWG/vB6F0tpsv9EtYpYd3duA3bQdYtC2VWff+c1sliT9Rl2d3yURSlGrfn2VbTFgpNwmQgzFNqrtIMzVmCEJCVzIAnhInRWgUXIT8M5Dkx6OQF/x4ysIvu6jyYcR4DZwg2WDl2YCpnQIbbXJFza07XaUdv+m6noAuAIdiYVXESwjoAAam7WIC1d0hnSGdw75Bh+8RA56fLopZvNPRhwb5sgKUVvQLAW4R8fHLBR7fiKLqodPECSjiukabABXSLbAcfRljwNZ0S1TVQXaNu9tcvHIWmCcwq5oUi4r/p77xrmiUPSwnchlkjRlgnHHzEBnEeiV4BCXut/4GxW4Er3jJqHWrMHPtIoIFcDKo16rvv4OliRRjmj06UrEg6iVofTrmq+99dA26WogkbPfcX+3lZ7MsWpl/ThK1PSAA6dsAxZ644mKyR6tr/ZNRkgbuN0pWyeAGoRkEkDlD1M4Fat5iAZkWTMgZHgxJtAdnxbjGBj7EIgAEi1q2pmwaYke3kOXhH24G9mwJgFi0fP3TbsIy0FogLeadItxC1INPkHlQ2N4MLP4IyM9Nku8V7tNOq1Tj7Yq4IwjY0iJNZrL4AQcgYe2tFPNs8TxqWam0hQ4N/OhV5NUmvXwEk92028U6JwaUtfEWr8Gppbk0kFGFLRbaqsTbfkAYixnpiNOq67mjX8tD7COGk2psDqRbPIF5+krTWDhPpZnyNQCcFmsuLW2W0y5Ll6g3guQWoWoESrOV3zWS6typvYl5J62coS7IVU5i3B+uqy7jleJue4ALI/ffSzPTuQB5imijZNbRDfVlVGG3qvuKdwWIZeX5vf1UeYOX7+6p1Ppabk7peFqsAa24+dIC6ctcmPi4UmXYQA6Q7AaSFCxsB1BtevPMC5wd1UW6uBNmTs3gNkiwVruXhKWJP3AKYrkFbf6huOOOT0HEv5KzPKKmwAJl+KqXOgFj11iGaOSJm0YhIH/J7i2b9GFtlKcplsTpX95LhlEiz9ZiTVBQRypzzahIKeiekC4esBo3vvH1moDexGKCB3RSaxcpAjM7AklsWNL/fsGJ/4PjOi/i1xiqe0ODsVoApZu917RMLPpa+WGaIWw70+94MiTeE1u+R0dSUEUYWh/gihzIeu7ZX3xuSwYzelImZdUOUcWlFXdXY913Sj28bBvFZ3R7jvBnwyCBRwLR9s4oMj/S3ps+Wv+372TZ12gEXiNAFo5U3h9M5jOSUwJuBAHLglYKzmT887keBnyzlUKDp/3bVMFm2kkaWUE6rGM4Wm6GIDxBWADXOpWZG0UBYTGlzzJ4Wit56pLsqQLA5HhIlmEmUoa19jY3TFEixZShYaqlzMNI4j9Y1UyXErcCus/hExPxrXI65hCJGhdEb4+bUIY0gnSLbZd83CFvRKlII7BUMuxUeUy1eYz4AZzZawbHxSJBooMeD1VpTMzedFqAZg9KqP2jNipTdnLTi5LpqZdJ1BbcOEcL9edcAMThoIAALEIzTXz5egcJQqDTODpdlMEJxtG9HFq3RtT/Mv7l7tDVEULV7J7uBVHLN3EpCq7LeMbYdxMDYztitPyIDHQpmOuzcpqm4QFOfOTSDILIKGoE70G/fwXJzws0778RGfZExEYXWVJjtnNY9fcf03igBB8U/Drt0bQTQC5Tigs2OE1EVyhUgW1/1WrVVi2ek2gIAd6uYewZawzq0rswYAqINPNQFN7wPIrY/i67/EIAGLtabFcu64vRCM1sUqJnFbl0sI8tiWiAWG6NWSzLrwb7vwFnPHX0AsmBhRm8dd69eYd/PoDOBFXkqn+HdQES3jfAW5e8N6KfMUGrd+CAKPVIK5WCmtblsYArrt5YeVyuRX8tlSWxUaMW+VAGoc+n8TTBM/RPYBnGDse8jSv2HQLMFyLbmR3PXSYYBBJ2IxJ+tOPgC3bkqYHTMVohuZ6ANoJNg4w0LEbZ9x9Ia9jE0BmRdEeBLrIYMEbp0rVckHa1DM3wEIGK0Buy8R2aRAFnR+w3aJxZ8uEshF5dSCrv5DKW0tyFUZgtEkgI+GgAhdc0gKx8et41OS4TuMKhMRKsHkgDYVQjJlgIJvvmV1yPYdvDYNDajuFtcA4T2QJGzm6VFi1MNi9vQdFLdUdIXMkxL0AhpszZAn9lJLuJjY8ySMXkgWjI6BScefAZfoKTMmZjQSPCwDeCMwpTVeoDiqsiFlJx4OS0auNnMBdPKpl+eZmzFvmqIXjPwAbsaD0SOuWbPBOvGupyyHHaDMiy3SlDqA0SETh2taclzjyEhdqFQiurYAicPwotiSf53AobtPEyLBta+aLzH7S2W1rEuK1on9A6IbfYlrGPcegs6hAGciNI3RqZR9G7ZotCcNeMCauJeSk0K09ZbXyw9VLNb0Bp2s0KlGeoQqxCNpjTeOY1Wgn7ItTFnjDR/72CTmQ2chR5V7mSXFIZPK1k9GxUsYhzYfmTlyLzo2X096ewGjwDIYqM05qmpxTJKn/s6aQpCmqYpL6eO9cULTRm9uVHGa+Ynt5jpuabFMkfFzwpAAoQgsRvss8QzM7oLtIpMkAJlisMClLd4n2oTp9UYSBUY5Kmddp6Yy2oR9EULrfWlK5g2WmudwDbpuoTMvVS1cwMk1H0/IRNwC2mcR8vfXQg6+Gi9Ge0CTRR4aIwBdH8aAoaBGY1jahYgbcKYWd1ZZt8PO4GUeShAWQrEDpBnYNpbdwtpjHHAfQw7zt1Y1UptJWjjGbU2iqb+uvt2RIB4ofegGV99/vL1rRbJeBF07bCvRwdUMf1T82e1+r1xePWA+DkeBDtghdD8/qKyY7ed01sjkBdHI93xWkBozWWv8UqIFZYrcvQN2ycYfEjMuTaydWZCIJJMcmLC1VIEr5fnFIZPDZQfl1gDaypvJPLsIeIuey0lxwLZOW5O7hKwegQ8Bsa2Y4wdY2wlywERtKmMuZQuF8Ywi8bgjBkRs0gEYZMHc1mKlZgFAShgy5+7AgOvD8J1/RnazvhvZ9LcdIG4e0TTWs28zysi3S4WkF1OnYKqVy8L1qU5FgA1M/P7nHDOj4fpadnxht4WAwAwKxNjYXU/iOTSXRYrd+6xDwuce+o1ieAWkOZZLNYPAllQjM3fRHMFbAhbgGAGBUpTNrXtutkTE3Tue8e6Lia7VgUapEW7hK32AaiADzJ0NQeYwoVNWcPBdwhh3SHfayMCJEugZAlaHTLPlAtid8T5vAezpgQeAUAKyIgeiQRDd+Ch1gzXIB3UpWttamJpuN4xVmc1deW4avYmqMvFCUvXpe6Zw8BYQPsAfC2ZRa4JQ1hdY26+npg+aUbHcnuD5XRrMQOa3VN3JtUKtC6k4It4rjlke124G8S+Snxhz94AzGaROhRy8S4CddhLxk3U3wi+fmgSjl6iXcBAB1rXAPW2LFHHg/sowaQ2gc0DC2192zVdYDpYUcDSFPwaULAlrFYD6JhTpwAkPvJsYEddGZrtwbZM1ZriAcAwF7JbfVrw9oro/MrubjhQaAATGDjy1kwJUZAm5hoxQIOjO7q8bO70fuVOBvo8vrBmkXlcVLhYyOjIZdEjL7XSFfl3ZQ3RxSfvlQ5IrQPmsNABTtzDrPsibmVWa5iIAg0d88zOUQXfkh6a/U0Sd/5F4XYJDcEZsSPcMgmen50pbvZ9JRavB6GrP4SKg4dmgw5RC4bGcIhmsLCZ+w1gROoCWyaL7TcQmgdgtuQGHjqhsSMq586E+/DiYAY+xg73Uyaxq9BzoeU0S9Bqdswcu69uY6iJzYNPWXdH9BiTtS9RStuZpwOiSXWDWNaPItq+NCy0hOXDNxubaivYNXuz9LxO0Xc3edIEHAlR64HINgSzolfU0GlJU/N5qH9fROsZWO0QALHbq2eIOONye74XLVLG6RtPJSDxAEjNWLMYFMtq1h1HzTJjgLE1tQy0voIA3JBWg932HWMIeGzo9ICxDHTRjeNkWeBVnSzeD0RWM8I2qnLmSkSQplYLFObPEQBnWSKtoy8rlvWExYIjtTpnB/l25yULxuM6wtJRNHfXViIIt2iNAUDgjDPfJzBxYIoJsM2yh7ReHps0/YdAoG7xFcYIZWQNhMYNYAVSDQTcDHBTbWwIII2wicZfDR5g2O6d0rXUt9O7rSM04HR7g5t338Hpxa3GMpzU7FwFBzlQhNVqge9phARevoT8HOdDAtNr9XMzYXt1EzM5fi6W28N3+RuXv4t7SxC1Hpg2LGIBmhZMKZumKe9LA2+aouyb1C1OF5bZIAQN6F471lXL/q+nxaxuXqa/o580FVldHzoY3fck6h44yhE/M2gHdwaG0v++7OrSOZ2wnk7YTxteic7lNs7o1DU2iw6CvjkPQ4Aoz2JxvtANpDvoJyLcnNRyhpF1mTYAA+l+jzpLZbdskpKZ9DbNaKU1LyKGiCHy2DuP9xhm/VNgUF2db97SMln0hvwxr3m4LDNjH8YHCsh2JVSfQb9z99EROP/iqfNRTcNVfUM+bKLKK6fX8Y3F6Qu0ElSa2KK+QAUi5lpx8OHHDNsYyZlaRPP7/UQsbdYAht0zrRsOEHLDoETZngFQA5T0XWtdMM7nDYPdVYO0VohgDLeOMGgVoC+o2R7K6DFpr6kYKgOJ/T+6AgTffTXLWff8rRfw4SnC1l/X3ILgPX0P0E3ZCvhopMCNBmE0gXQneOucj7GDKZt/T5PVba/1eHaBY9fVe9pvViDHrag65Oaao9Q+PDDO625EdcsG0E4ab+Rl7rddLTZ9134rXAChmoUv36u24+FxmIRUWit899mot2Cb0wUoNLeLgwwHl3Wd+PbvITApYEa5YwKQt2kVqDh5qVXkAFxizVL5veVItVhVMWda4Er0eZmBZQGWjsG2p4evd2PgykRNGpcxaM1jFnqMo/v8w5JgfVSLhpnWm8DN5xfPa/+QAwAf0vgxDpj4lVuM6uf67t/P3wkioNekTPxGatRjQIuQdTF/fUdnHbte4rBSI5egiQpWteS21szx7JS5mmh+5+saDblhX7cYJ1MQve4RDYe6fmzu0aJF3GxNWOCoB3eijmsdVqdzX/OtbCXgc2zPuy6r0Tebm2WHbn0xE3wFzhcxTsBxydTuxJqOyaX8Je0EDjDyu0yUkKDl661aOZxep5/rEa+7gq3X8qxE07MfabECo+o9qJ9f1z7R4GP2rT4CMIhip9s05SsBsmseQFTEFP8sMLcJQ7YRgEPLPluRr8GQPd0qMgS8Z8zHOBv4oLIuDC3nvitqnTifzxOCBKrrJy0fDmCYvdiYb+8+cH9/h207G3gR7PswoCMafEi+l0EzvyYBrVu+t8aLhOAnWO6+ySfd3lDBA1Hk9ZOldWp8xQm+i6RuB7+oVtkIva/G2IslITQxycUgqt2671z7bUmVpvXSrmlfi6jfUR9R0Bnhg50XBjRHnboJe0PsIXSydocLsgEL+rBS22DSVDMW3ZhY1beMI1mViS1dx/h0c8LDwwPay1e4319hGzvu93s0IvC2Y+0L9vWEZdFqqR54N4ZSJVOWkBYeABN2GWhCWOCBpS7Y1D0lrWnmzLLovhqnG9tjw8qxW4CktOYcBX1ZE2DqgsnsJ7NQwXP2K1d/AnXUtXhtXb6JpnY8RhTtJtN1wKnFS8zyZhrZyuGH3iwlfWxd9UVztQhpxkHqlbB05Y6+LpHeTK2l2d0BX3NLo8eMNKVhduFWYCFRWlQMyXr67zGqRgo/Oo6fA7D6fvy9vrz6s7/XY4kIjQm6cZ4KfrG03fW0QFh3O+XeMCgz4zzQGmI+/0ZY1wXLzYLTzYp+WrCsCsLF4o6apd86+HDe0ZuBPM9CEQEPTYsfW48tJNgqAbtF+OZhxdg0mFyaBm4GOLaiE+TaXcRgQOm6aXA3iKIOznqy2LACPm5ONyColt52BR6x2y81EEbwlSpK3S17zQLiyhRTWUfkAMTOt6WmoErUiC5WTEw8AFhyH5cn1lHinkcQ0NS5Qz8nqx2V15wcMCUKVLoDdJsPz0gsAOQXV5Gxyfpx0J6AItTse7dghAUhTrbjJSLsneBk5HbNEi4Wt3R4UCnny9wuZm7AsO3s1SKSoMP3HImNtczS4U/k7oXd7j9kpEvHr7NvESPy8PCAfd+skqruI+N6oe6NbNlAZKbWou35d0RV8GfacrhkLHbDo7hVy6Yop9x6jx0udedVc5uY5aMyhmqudDOy+lopLBHd9uRoZHEmQxQaMKEPc7fAh9osVOK1TmwMwrKR9TwAhKvNrQWq8Xv6oiF7jLQ8FETvwlu1bYr0Sy9K1c0M32Fg1mmH1KysGqfr7RRbT2sg5qUKp+BT39n/TgKf+qNaXd3WvMErEepEt3yGlgwEB3FIrjTFB+vLdFBZO5eraW5FYE5rtFz/moD17/Vpm42MBymn5UMLg0FLv4uuv7Y08MhgXfOJpP/d9jJwiwd1BWk99mg5PKONs6nToU0DBkYcQNA8nm4tnEeo8p9LMJLDJjlG5f3I966N8VWlLLShHA/qCkgiEPSQheaAHkTKg0qhve6WNgO1UXrcPpNtWugBu8E3mvETRbqx8iBdt2s346hbJrx+TbdtA3yXWpDF4vgcC108bwS3Fiut8qolaxQVvkc27tJ864EiaMucTYBRgo3HDDudeMCp/12BfKbQitHk8WVlGODFxmbQM9HKI99fa0qTEqnZrnzUcUsaLy8QjiBk6oPMgKMe+4vC8uECYXpskXnwiwlMHFSICYLJVIYwYfmgxSZuzBjnB3gpZ/Y4D9vi2wNNYTvO8s6xuZsqVoz9rNYIfd/DxeJPEVpMLED9ydNvz+d77PuOfd9i8zS9Rtb9UM3BS6h79L0RRtM6CIhEUjIQUqwclNpMbJXdrJCWgw0i2zgKmsq2NPSloZ0UaKy3J/RlwXpzo2mrDj5aQ6MlmJ4KgkTLDgYVNasbYmnN/Og9Ku+NMbA9bFrwhhnUdIdTOVndlJGAbds2KxM/1LLQFsQW9R75CujfFXzYImkAhgxsW+4MPNjqrbgm2BrggXVR/8E0BNYCT51ZN5raLegYwL6z7sq5D639IAK60Y3ltn2zQDzL6qHMwFIXHAE8cgMwC3TserCWUu9LpjnaXiRZUrqlBg9zWR0FlAN1b5TfH9fgtfYaPSueC3A5MSsLMzNryiBJq3E26ip7ReN2zBSh54EA4tgHpBHA+wLhXQNvhUGW1aFD55lLCj6Wtan2/uIGy40Wy0KjyFqiC0ZsNQzseTxhmTyAJ8YjIQjM9QHrQT77DPAOg3UBPOL92rHXWkjE0pfmKMH2rBENOF2sKN/mriZR5cotxcEzoO6WZenKB3oLq5KYgtEaMvjZwXpT/tPj/jYbofw0cGfI3sBtYHlYIQzspxNONyds5w0n26Sxe8kFB5ZN42e4WOoaUVi03DW8moLkextFVVqiqIasLjVARiku5qCqyo86xK5H+hibpdXBJ4UlplipzLKxM2v5dq8Xwowh+trtpVaP3GBOHl2Fj5BAmX6nS7IkgsCMBWQ0SgtVd2W01TUwAxARsYy/6xaSXxTg4+ii8BZ80gmDM6jLd2SNDdv8N0cf4kGZZpnw7aYtdkOzWJBxHsxayplFqwQOxthy11k/Zj9reu12PsOLjCHIxjR0L5cujH3XmiFnAyv3D3cBPuIZpMYk6wRrNsWa8SJeRJMoimi5ZYfHCCbSl1IuvLXUSgqRhcnU3C7SSYHG6YR+o7un3ry4RV9XnG5vNehxsUDHpu4TMobH5kIKVG9gsA0LnhVkDIY4HDMf8yLgNiDcsJDtADy6zt9Qc99gLRVOpBYgNZ374nJNNXKbAEhVAuG/tgIQfanrcWaVic2zfGM4K4wkgrGdwdsGbDuIGVpRwi5lMT3nfQd4YN/OAMTcV0uWgiYP0BPTxDhqywSoduW8BNZGcK1pbR4vk5qnC2AxoeqLBkWTE0zug7K4iA6aV5V3T/FCIiviVphQA5rPsVyuX3XPmZWAVOvTsWhZ+Omg6WrFUWWq+3kBDa2wCda0T7cgEWXMhwYLN/S1qwthVZrmJbdcD8uSAxF3X8VaVmcuSnXcqZkVSWOppICnp1sdkyctGleHnNJiUbsSlolUCJpllHh59G7WhrC0mUk9A7OLYKFmtKXuEPZ9SUjAVnDPgUezd/ECYwA8naYReQ00+P42beloY2BZPKh1xXqzYt1WLDeLWkp7M/DR3KAH7jX2qYXlxIvF1SrJHijpFiWPj4m5ppzP6s717R5cfiQYSEtbWI7LXHPED6p1dgzWbEbWisn74AAju+geMsovgUFp/dT+4A1NHmKYtdLCDDoK4RReSNNv1fIxWcdsbGL8YOPjY/uW7f8I8FGDS71N4MMIY4wRwaCRJqoHF/Bhrg63ZDBD9s2Ah4IPsdiCa+CDzyM0ZVhEtIKPtFKwx1eYEV0BR2rYD/dq4Tifz9jHjoeHO+y7bprmQsPjJ5aeJtJlWbFYnrxaFDIoCFYNUgye8xgqDBrQxbM1rF6HMdtuq5iaM1ryfDt4kaX15oTFwMfpxW0UGlrWE9b1RgtgmeXDhd9upbxjW+x9hEXK50KN6S1kv1qmoNook9XFMBun2SGJ1adIo1lKmAalRlGkCd+H3Ii5QDGhHpPdIqUWCA1uMSa9rD2AWhOAxKxc5zNk29UCYs/lFp7hNDh2TQeGlo2/ffGO9mHRGBXvLsEALxkNAqYBQ+e2pclcy3+XzJhaNpzMBSMWPFvXTDCz1Kl8tKRoSm9i2qjXDWWrMCrdanwOVrsKPqKPaphutlwTlDZEBLD4/C0Rr6SCS61T4NTKHXxQBJwCbSHdwO+06uZp6xKuKhcgYR0z5jw9JwGa+ntdElSQ5zQeRz4BKI4BuW8CPBx0PBYXkM9gg94U6Dt4jb1ZDGzBwIcOcArqi7Hx1FqCgpAmFl9T5j94yQxvPQ0YYnUwmgXxtpaAaO1YT4sWNdtXLXfPkgK+eT0aAyIOQBYHVQo+FtvbqB/AR3XHi48RkvarpUFQ3LAGaP1nUkaR1h6q56bFQ2PIFGy4ZTVAB2tp9bCAIN0uusXR4/NrXbK+H384gFGaLR9+ToCOMndh/TnS0uG+tV9vA5Zr+8SCD2fixwmIRxRY9A5HqqwLOTFQwCHs8hUIdB+RZkVjWN0AtiBPu8aQBB9jLyXU9Z5u6XB3T5bQFQzWYmHuUrl/eAjrxv39OdJllUh3eK1/J4DeFSCEz7Jp5b2Nh/ksaUKcbvprcNOiM2gHHhQmtdDgW1k8npe/aCqoWz50US8g/7wuaOtqrwV9VXdHX7IGSAewSGb1jAK8okCbxQGmq0xsS9Ul/PnUKvjQY9QEK2jcIA1YoMBLHOgBqim7BgmAmKwqaJpH3fTZ4SBRsECDFKl39E64ObkW1ZLoxlAX3P095OEBeHgAnTf0fTc6ZPC2Y981yNgZ1P3Ll1hPJ3zPL/lenG5u0EjMldNN5DaAdwUSvJigS0uOaqw29mZBcSYvzgibg5KOZk6C3hdljKLjk++A75yq9OPuuaL9XVO3rvGYCwmNZICFMblFyjGVu1HF7MX+WcgKX2U9czvB3Erimpygn1awnIDeIIOmeJlGeoqWAtdy4H1V87yD5tG6gXTKbKboI6n1xBQCcZR2tBj5uytJ9pxaHVlsDI4DVYYvrEDz+/FzjZO5ln1Qz4l1DQUgZJr+si4Ar9hOqxZXWxfslukxRNfXctJdnXtx62mQ84J2c4PW1JKERQOg0TqkNQUUbv1ya+QkPE3kmbuGrcx6t/1EtCLqiuW04/bFCzADdy/ubO8sibg2Wiy7qXUr/d7NMpnW3cmi27SP3gPed7MmeH9ceXEx4bzcrdg5de42MhOR9qG1sCh6TMdg3dl8MGMbuwGQHfuQSTnTPZfU8qGvyM7P0JYrZJMWzMsFKYDy2XZ5jJQ15+Cq0pePYQ8LeSq/02eiAC8V3NFbuF0+nr3E2l/8i38RRIQ/8Sf+RHx3f3+PL37xi/i+7/s+fOpTn8IXvvAFfPOb33zra0/CnH2313yvAINH2dCtxHOwVR4de76PfQdv+j62XTeyst953+37DeO8YWwb9njt2O14Ldq1h7VC/x7F4jJ0++LtjPv7e9zd3eHVy5d49fIlXn70Ei9f6uv+/t6CSHcNWkVFoG0GCxZE6Hu3wK0XHqhlBND91TuWpv7dHqZ5lFfRaArqbTU+wqPE3afrKbY9g7vCjLsu6KfFKkeesN7eYr25wXpzg+XmBsvphL6uaKc1BKhuVe7uA9dkerzXrbw93gK9xZ4OvvkXdUIVlV72PMO8LPuGy7uWT/Wal/Baf00EHbpj5mLxMYuDNSgowhiQbYNsu9ZN2HdgDK2yOYbS07Zhf3jAdn+Ph7tXuHv5EvcvX2J/eMC+nZUBDnXLkBj4NVceWdaTayrKy72+SqbVuqBXK4kdSB7b4inQPbYkqLuUBkig4MHTd0dtaNaMyquYaKcWgGM+zwMIXTg689JAYYp3v76Q1WwhpIXHLT4taUCaz2Bhrv4cvo6Kmf64e3CY3Y9afvxd+l2Dm9uVMakWgzj++li+7asKhOPr6jk1EJfS8uGuinlfFAUovS8hzCOt1mLE1A2pbiuvSOoWBHYhTlmjxQV6EGmhPcKxn8lT+rpgXVfbMC6zziTmpvChEuQKMhuGzWlYEMsruhOWvrKOUAAdu/uYzQ2Ow7we1gQQPNr3ccnNPz2TLhVU38W2AoH4z0HCm8jKwv0qsIDk2F+9lhtyCrC6Rm/1PSznT9D0m7aPbfn4t//23+Kv//W/jt/wG37D9P2f/JN/Ev/oH/0j/N2/+3fxmc98Bl/60pfwB/7AH8C/+lf/6mPd59LyQXDTvRSw4fnjEEl3ilky3N0SpdAHQ/ZdAccYIVTYQMS+GRix1FoN9swMl7B0TIBDXSvbtuG8bXh59xG2bcOrVx9h33c8PDyE9r/tup2x7556E7u49pjkTCmzLAxWn2hvK9auW8QvfdXnFdv/xLNyRABo0GJnMhOkAxBNg4tUWgsOjaBF0yr60nFzc4ubd99BP53Qurpd+rpqZcj1hHU9abrnohkwyhB8v4dVXU1jWDE0xn5+0IW4swnfAd7MfbUPs4SIoueodimxQ7B0Bu3qk+4Q0MJoi22497CH0IwWKNy1VYn4AeIdEIbwrpuT7ZtaHiDobUVnoAujC9ClYbeUaXl4gJw38P09+P4B4+6VglRLMRMRyFnBx7i/w4j5OUO2E169uNWCZB1Y+QaLrCB0kHTQWNCI0RkJMFpqJD3AlmqjnjWjoZBp6la/uGq/p5sbtfQw294UOwaZG2wva6ZoW4pDrjORoxmYZkPAk2biay3cB3YZBqI0uBsZlEHaHwaiPbaDlgXEK6Rrdd5N3G8qAPnutKRlwf3VNQ3btVQXRASJwF4XXnB+Q7haQposYDaqyKUEm8AczU6Yq+N6zarh4/PUfFQLsccyoO72xYjMHl462li0SN2iFg70DmlqA6TWcPvihbqxOpl1TlO7l5sTlhcvrOK91eFp0I0MAZCXKw3gUQKfY759TN2dRnqvoc/n9T7WmxMGD6w3uncTmFVBIdIg8NZtT5ceqf8et6aPrPPbcEmTsyDWPsthfJkH2LbB8PTjxcoJhFktsmsSxPgc6D5dw+I9rChkifFQt0taO3wT9LdbPf40V6wf4q5HteA5PU6WD1/7iT607IErsj3jgo4gd7FI7GO8x3ccfHz00Uf4Q3/oD+Fv/I2/gb/wF/5CfP/+++/jb/7Nv4mf/umfxu/+3b8bAPBTP/VT+LW/9tfiX//rf40f+qEfeuN7cBHyIlIKhkmYjNPKYdkRBrMjkNQ3gItqomKgYraOOPjQvVmGWTiywqkX74qUW9Hfdsu48LLo5/MZ5/MZ27bh7u4OZ7N8KODY4zzfWIscpSOtEZk6acGPcAUz08la0UpQTLtSCLFZsBeZ5hdVTQ/3m8yzcXxaPrRK4GIultUiytcQgnpMKe1te4203jUNtTUMNKBpIbU2GEwCbgQZ+psMtlITgtzYzN5Zwj1AMK1HEEWDXLuifaTfNnbMHSFIySM3w/cZfh8QOIJRBZb8IwyMHYKmkemW8jzOZwUf24axnRV4PGjsjt5bs5jUquGCEMo8h56/94bz/RoyrpmKwvuu3y3qeqobhUXNhJbaK+DC2uSeMcaqoYVG73SEFPh+XFCN/S64zkSusTnHe840q9smzgt3xOOClIAQAHo9E9dEChgFUasHPu9EoXV7iiY1grD1yMh6WdfIepg2ARONV5g04KLRcXCcGBQ4AJp7DngUUY7FDDYcJuZIFkFjXTjguPj7OFp5noMxKn30c21dRxyH9qC1FlVzPUV7HwPnbcNmcWKv7h+05Dwt+kSkq2WIbdFufEj3dVGwm53WbRCmMQsQKbFnim866IJQhzWfNDIwlkUzyhZPu7Wxas6naHol03MacUuMj7/yX91eYcD5R1o70tLhINPXg2frpKvai5c1iG02B1hNE5ljv4ZbQMpvaQEp4Ns/VyB7pb25iM9jL1x1CTvma16xYiQPuf6b//02wacfC3x88YtfxO/7fb8PP/IjPzKBj69//evYtg0/8iM/Et99//d/P37Vr/pV+Jmf+Zmr4OPh4QEPDw/x9wcffABA9yMZY8kB8z04HLHZLrMOMiCW+iolODTSac1KUawivousWLCPMOPh7g5jG9jOe1hXnPG5JYW3tHTc3d1F4Oi2nXF3/yoAyP35PjRNAPCKjdQaXpxuNcqcVUOLPUWAqKehKZ0OPsxiYVUDqXdjbcbOxIK2bMFq8pcyQxGO8uqxh2UItGQiHItav+tWhAmkn5ebE27ffUc1oNONmb0J0tVlQn0xjbxDegfbZxJBa0OtGb0pgNsFwieNYeln3ZSPAN+sj2yfD3GLFgDfCY6I0LpujCUiaAujbbsxNZ3HbbOso32gEWFt3Xz5PQBfp2Fmz4GFBEuHanA+DzzAD2m50NTegfHwoJaSV3c43z/g4eUr3N9rXE/6aHU7axLNgtFNtRjggfPdK4z9jO38gHffew8v3n0H7bRoHAcIxCd1SYmp/vsA+sC6rFrkzUqrn0432Fjv4b5213DGGBFw54yGjfHH/LvgQmH6kmB/WZYJjAABT43m5nVM5XW0hIQxoPiDL0y01hcX4c2AgMS3gG75ae401qhcJtXCX7z7LnonvHz1vmWySWR4fOq9T2G9uQH1k8YXwEzjJZ6DaDGXY49+Ce/wbclBMF9/jbNKUByARMo4hN27fleAmd3bBbVQyVw5WE6mFsJDTxTfK8RipAgak9GpQ0CqwVsP1mWF7AKQxgbRsuDDV6/w0QcfBlD+nx9+iPc+82l85ns+jdN772FpHQ+7rmO+37C8OGHpC063avUUy0ob513f2TeF0H2elIepq3M397Z4PJ670mNM0uLTl46bd241CB2Ap9DrNBD62otClkURicx907o+p2+pAACWPgxRx+zYPS5NK0Vv7lYvfao1TEC6/5THv3lqL20bsG9g3GNnYBuMzRTZbbO6TJH5MrCPocfsOzYDPBHzIUEVE86dIe915aC2qtx6072PLKGDckUHzQMTwKjKxBGAeHMrCTBvZve69tbg42//7b+Nf/fv/h3+7b/9txe/feMb38DpdML3fM/3TN9/9rOfxTe+8Y2r1/uJn/gJ/PiP//jF91KsFWIWjVh0/tsRfHhJ8V23tefdU2KzMBh7PQdzCUSRrzGwnTcjQnWLZCF+CWvIOG+BZl+9fGVxHzv2seN8fsBmMSK+MR7cyhG+s0OKKyy1zdB6RN+7pYJystvipbW7AYxu45IZAb154KkxJMmIcGVsEgW5wtdumoBEv9LC0s2K4fs46LFm8Yjflyn7Qsuf1BgM01Cb1nKgLgrGmNFX24dBdF8ad8OAXagakyLT8I27qzXMXp0sDtHog0qsh7JiqLkkmb6bVHmotcFrjVDk45pPdjD2bVOAsW04370CtgG6e8D2cMb5lVq4xhhhQfJMDwARE+A+ds//Hzyw7Rv6+awxH/uuYyeM9eZkc9xV9BIZ0zDNypUiUoasmlhaQ6QwjGECnwuw8EblA83fFE3xoOhX48PFor34EKDimjsmGdrhYsUKkMDI3srhQuZO4A6xwOj1dAI2SwG01E235HWr1cJGQwEayitN6N5337U0H3rS+mq3D0YRKgDClQQ/P10qhjXsd/9tOq+M1zRM5IWt8rrZmq0VXV+6AytF7IS/GMB5DNyfz9i3DSBoivnDPdrditP9vaYjv3OLhTs6SPUA1iw6kO7irOBD6+6wF2AEAHQDQghvWLhbAEuRL/Eslh7blwV9MHpfwN0sHxYwTr0HL/QYEeeX8JVvpoRmgyhGVDmqZnlxK0TEgvFkibiInwne7JaWBJ4Sd5eQW/HIuNbcVFWJ2hfY1RMOXz9yEGDKaeHBcJ55WKZFOXiTdg2UfNz2VuDjv/yX/4I//sf/OP7pP/2nuL29/dg3re3HfuzH8JWvfCX+/uCDD/D5z38+wIKwVSmsZbUdfBTXiliVSbiVY2QsSHXfuKtm95iDwdjO6hrZH87qRtmziJfHdJzvzmrluM/YjY8++siCRUexpmiRMW0mFNwiYZpY7CNhjJcIsZA8lz4JHbEwNTc/q/U19HA1maRW5mBMk0yz6pEm6lpei4Ch5oF7nrEZgYnKrBfPrmgGPpoHhGr0e19VUNJipb1JCwCp0MuFJWgQ6oBFyhMzpDEWADJ0u/ux7xi0WWaRlY5nZVLMBAyCQDNlqEu6DoSBbsFhNKxSICsIIYLvytgsdVcgEPaCbZuOhQXm+r4XIoJ91xL25/sH3H30Eg/393j10YeQfUd72MHnDduD0gULRwn1bmMBB5zdStL3bhtJKXPetk0B265zzgbATrcaT0PJbs31l4xMWbtqgq26vYhCUAqAwamLHMGHCtcCRqpwvYIJartqrn2ikd3vddd68n4OVBzMQsEHYdHS3acFN7e3AATn7Wxp0qdwHy6nFUKEXdiAZhZlc6saYGDZBYMLLMckR4vN4fnjc7Es+d9HLXJ6txsd3VLukji2i/Pp2AuZa2kQadl9QGMVFstQIcLDvuPV+Yy7l69ARLi5XSHLgtE7lpevsLeG5b1PQRbGIgo8aAAshCYN63oDYcEdzqrBD4laSyKCbqCHjE9RQZAEDV4U6rYNgfLzZVnA5V3T1U0xWmxPI7c6WFVTCasUu76StBPuNptQM/WKWwHcNeLyxbsYWcfp9iSioKkAHhT6X3GhGJa4WEsNWUnnAD7eCgpctrhKuZ/AQIekY17EgRKKJa1a5A7XvWIN+ThBprW9Ffj4+te/jp/7uZ/Db/7Nvzm+G2PgX/yLf4Gf/MmfxD/5J/8E5/MZ3/rWtybrxze/+U187nOfu3rNm5sb3NzcXHwvtj2978ya4CNLoIvtZCnmW5ehx6jlYwYmbO/DwMLY1cqhsRpqUt92BS1j6D2FJawhrz66w75teLi7j2vc3d2p2U6yHon/17wOQWy+tGT2ClLAgWDlyy04zoN6HHwsltbUmmaH2LGNGjpp8S19fsriXbAYB49psGvaXtGRE9+iameDVzmNIljdtA8rxhQ+1ynuYIn9MqgvGShoPl+ijD6nJuEWgOftowHUdav03kHnphorwbIcJHa+BEjnpJlmwzbSxEAH+s2qmZiDwLyDiDBgcqUD6KwMyO4PK0ClG+BpEBi3YQtT98w4nx/w8KBxOx+9usP5/IDzeQeGgSabExWAsLRoDYAzc1eWeb45RdaQGEjDsmiKIwsgmkHDvaHd3WHdBvq6Y7Hg2mZ7Ct3Y/jUIUynpPFpsTmpfsPUSKwoRfwIbmCtMprXU4t60OUOL81yTt/nOmITDVd3agDBuRb0RQYllcKuDlNJL/p1ZzVQDXnC6ubWdpFU4MnQPnc6MRoRdgH3sWBdgaQ1Mlmpb97UwtOHBhUIufDpmPfYI3upPFqsSIzF/NhuWCgb75MfINAM0f3ZpFlLNqsO6YKHsnyoiXeOr1NaucLYzaFkhvWO0hlfbhg/u7vDz/+t/Ks8E6f4364Lv+x8/j/c+/Wn8P1jw6c98Bp978SlgaWjccH8/QOcHnM+2ZYQ0uBVWRNcy7xZX01sEkg+zTAtrJehmcXUBLlq3jBvRYoYiWPYR9L6cVvTW0VbPvltihN3S4CMZoJXMGkwEYrWJet2NwcPSYHeN3zMroZJYqY/UUwHzQFO3fjDNVg6PxRKzuqjBR0pgtxUSs9i2SJhwUr+60t6wkcoM3dtI/P8Z/xhNV2Bdzweug4uwwlf343cDfPye3/N78O///b+fvvujf/SP4vu///vxp//0n8bnP/95rOuKr33ta/jCF74AAPjZn/1Z/Of//J/xwz/8w2/VsbBcSAn2NB8hJLNPvDKpmFCZioz5PiySBcgSfIywWuwGQvayZ4q7ZM7nM/Ztx6v7O2znDfd3d5Gq+/DwoLUrjGGG1mnWCnFTXUlZy8nMifffM53S88gpajlkVUu/jgZ6hkblQXkeaCotVoKDGgmfafpKA0wY+NAgtFxkmXJX+h4BqZ55YdaQyorLhmANpp11qOC3KlokAC0IydNE0LZuqJzArP557JZq2Qzg+ePaZ1h2jqlV5mLiUGo9kFWIo/qkD44HwakrxuIIWOlF45HOuH8442FT99o2tLx3g83N0pNJWUlnBR/tAD5uYv50BAjSTfscXoRtAPvAw3nDYKCzaOBf77hl1kJmQTspoN3X3chqLcCD2EyMid8x28wuJOiRjCmNx7jfkc9cMYGoXHTBWDT046WqpSVtDQWA2DoKs0O9NiV4MYtd601jDCyuQ3lHBvk5gBljYBUpa4Ki79lP8S+S4Talq2vj5n8GHw7DSX7hmVYGw+NsOYxTAgs/2j/OwINi0P3ZDkAIWg69kdiOraaYGL14fY6HMfBqO+P9Vy81M20bIbRfbQPvffQKn/5lvwzcFvySjYFF0JkgmwH3XdBbw7osai0WilhrhrqsGGa9NguxxuNpbF3XxaxKpj2Tu4Nb72i8mOVD5yd3qnXeFbk0CbCdFitwI4JbmGD0FTU5SnBojfPR8aK0dpAra8XyYby+Wj0mK4iRa8DC+vnwTuT0gI/dqNDzQS3A8dvgJ76m6jXK54uXxTDW34/nva69Ffh477338Ot//a+fvnv33Xfxfd/3ffH9H/tjfwxf+cpX8L3f+7349Kc/jS9/+cv44R/+4bfKdAEA2Qek7xY7YUDCTGIQBx0Slg0E+OBSQKzU/7Ag1N0CijT9U4u/POwax7GdcwdaresxcP/qAfu24eWHL7HvG84PD7m5nMwMOxYDEfpiQUqdzNTu5lXbgTZcL7Dc+X4AGPaKmhaL+jmt4I/uYLuaEBczI8LLaCnFmytK3QmFQMJP2sN8OJoYIy/xHb6oDZCgaVqem6t18aqG1zzlwjTE1npWPzXrzLZvtj/OgLC6UHzzNfQFbRH00w2kqUtEwGiDsImABiHKkDuiZwIG4LVQFBcR5DRUg5dhTE1DbUnSFwzY8AwJ65pnWJ3N+nV+2LDtGx7OGzYW7NQgJ3OHdA0KJQdAIAvQJeyO+wz4ETVsROjUcDrdZMzGegItq9YHgej23tRxtwuwnyE4a9np846b+wf0F+/E3ADm4yZC76VSZyygEsRXxGkLbRmpYonWOgFBrXJILe7b0a67BvI3f/f7pjqWAARAZBsICJ6exGbtXJZF15YVbqOl4/78gId9x+nFC1Bfse9D0xt3Fd5aF0KDWjupwANccPt6kQmAAITJdfVtGpdrLpSPl7ZsfQfge69IATOW2wWsK7CegPUGZxBe7gM//4G6kXtbcP8wcPew479/eIeb2xPOaPilv+yX4WE0/JLv/SX43l/6S/Hue5/Czc2K9eYWwgPf+taHWj+HOVy+q/ExTUDiLGnAw3YKH7h7OFusxcDYhu3g/RBVoQHMG8OZQtQjw84FJ2m6rz+vaYJZ11iFsgPSMUTLnO9aXmHfRxSH9DWiXml1Uy9GW+Gj7gW4mhJUa+MdgXpW7kEgErH19+1aa35bcsuxmT1ql/x33+IhqjcfLB2PuVi0nlSf1rN//o5nuzzV/vJf/storeELX/gCHh4e8KM/+qP4q3/1r771dcK9wuV9Ah8SFg9RiD3FgTjwcAJm20DMwcfYd+xjYMjAeVNz23mzOh0PZ2znTcHH/T3284a7B3W37NsWQKe1JRlTKX4zxWwYcWYaYTIzAAkyKnMr55ExRQ/wDPDRGnQ/lUvwAcDSAAQgjgJjTbqi6oLYk6hQyg0nkBBbwBTFq5ToNDLfF08RVJSalQpFdQcJNTRmjUOjkv4nanpURa2Z1iqQgdBgG7tbS/d0idx9MoOmaUwKdKr1SBlRxPr46Dj4gFcjlIh4H8I4W37+w75j2wfO+8DGjGFBxM3G3BeyB7N5TA0P7d8QBg3VkhYoAz35uEJwAmERwpABd+23IdgAi3USNGasIjjvO254KNM7cDVCuihc4yW38sTjVhbsIEXCQqBydmaBTzHEI4x49Fibg6c0ohDycd1Cg6CoPVJ/8mu7/UrrPLCBXxVM57Nq8Z7mqD79MgYHrY9KD7I/uU4u2rWHLt8dq7DXNaJfFIuFD8HRdILiOgu5mudLXE/m3+p4+jjGQrVnMp7CpC7KBxPCrRHutoG7M+MsZ9zvA9/87/8T2wA+9Zn/hpev7nF33vGZz3wat7cv8M67LyA88NFHH9i6iJq9tj0EVPkRX6+eBKDul+3uwdb2wNgZPMT2vRrhAr8aXxBkkuCjkdv4nMopRsNta07znlrr94isQx9wF7bk1kXjK2hhkU7wUQmzfCR3pl1BJG+JOI4UmKvm+MMR9aRVsR5S14B+NwOQa8AjLR+tPOIlCHmT9gsGH//sn/2z6e/b21t89atfxVe/+tVf0HXHPsCtVi3lIBY1YSKtHh7PYQGk+6Z7rexW/GmMMYEPr+WhxV4G3n/5Eudtx939A87mWjk/nLGfNcDUd1uFCZxuqPFkyLu3ZHjkMR7haqlPpUtgqkjYLIW2exU/KDo3xO3BYW3VXWWXdcFp1ZoFaz+lIDGVMYSrppxAdkYfjMZshZksq6VbGpqj+ObacH4n9iKL71gWSwNtHdBIBDBrDxRANaBZHZD1lGloPlciGLAgSDe1UjLp1hv67Y26WwZrMagxtKAWD4y9hVWLtt2CjFV7Gg9nvT/1tAhRN+vGrkDDgZgtm12AB1HtZxtDq9WygY3BuHvQgnH3D2cFqKWeDJGDDw4hCOj4biOLzvl0uBWo9zUY1u3tLU6nUwJT8kDfHrvs9psVL977FH7J//1/Y3n3U6GdSxU8EwNRrcTL0LOF345hPXFBZ+d6aKUDEM+0OJqEa3tz9oLCmJ38D+bfAOVGAzmqKVzsVGpNzfqNAmw7iF1vFnCHuuDWBe+88w5evvp5vHz5Ct/3y35Zzge5xlyAh1s9Yhwl+vZW8uEAPOjw0/FaUUQtHrAeb4AiZmg+5/i5/k15Ebi/0j3WgK5BjzmimxN4WTD6glcsuD8zHs5nLXwFABtA28BHP/v/xen0X/H/+n//f/CpT30K733m0/ieT38PXrx4gXfffQfL0rB2zZRZ1o61Nyy9Ye0dnRpuLFh+XTtuuu2saymv+3Y2RXJYrJ3HeUnMUa2Cq/qGWlea8eT43Ut4iGXxUY694gpLYmCJ1Np937OIIOfc+2aczaoEu9WRoRbOsHTYeEYsFxlAqcDkyn5AEUwsr19TdJjrCtMLNk1gBdtsQiSS/Oq1aryKL0/nPbVidn3VgmO+MzBi7JOnvmn75O7tUiwZseOsgw/z4UqtOioy7SGyO1FFjMcOKZaPfduxs+6V8vLuHg/nM17d31tcxz328x4l1b3wDEFN5ziUZ/ZywrUIjSNjMuJz3YPgRcYsHsKRux3nCLkZcVMtX75q9kn3iqj9FAg2o8iNoG37eSFGbwM0hnkhRHdKjcBWt2jY94ZqS/pLWj28CqplvEipZuivNE1D3VLFBkle4tl2XRVAg0+B2I+FGjRepZk2K0DrbFglXQn+bCLNruFuCIDNTdRbB3UokIGycna/u1hlQQHOzHjYNtzdP2DbN9w9nLENxqv7Mx62Hffns+Xqc3Ax9gJmZslCXB/Yh2vaI/XnAGcthPvN3QNO6xLjE1r70k0bYyy3J3xqH3h1d4+zbTyYRYIQlh33mS8lTgcAdtnBTFEXIwPpEPdNYSXxfNGn8mYPObcnf5tZailndvUw/2U6ypg7CUVwXhwcHDn7GzUi+gJmwbbtGffBJd3UL3QEQ4Wpj6KvXm8FVF08mUzfpeZ95RKTxcJPOFhFSlen6zhgRL5rOqXEvioV3mnsvgc7+lrOzJdBKkDIrRVGl4PVIvjRyztsO+P+YcNHH77Cup5wujlh6YTbVRWpZWlYFxVgS6MAH+vScXNa8eLmhJt1xWld0BthaVoWQKcztW0AGXNGObcsArLNJ6UE2DqYnlBrGU61sNJc5MstHm71KII5CvsFe6Pk0fZdAhvJz24xpuSHlSZq7ZuZWvwxroAV8UeiAlZm4O5GLQc1qRAFRg/gES6XIrP0eS+rmVZwcc0C5ccAGbD+Ju0TCz7cmuFxC+l2yT1dalCqiGAU68a+6xb3AT68emkBH14I5v0PPsSr8z1e3mlQ6fnuHOY/bwugKWNds0R606FTI4MRGhJ4hHCnMmEAfOtpaofN3XxCC5FTI90My4p8rTcnrOuK9eaE3jrW5ZSEo4El2icWYLex2gfatgO7WTIsRsFrf8AyXciZcgRT9YjdoL6oi8fqeXjxHvbYD6KIESFzCyh4lAgeVFNlBzVBI7PCqA4GjZ2w1GDSiHxA9L4gdGGwa+4AiCybiUjLgDQBtQXqqyFIX0FCWJYTGDuwOJCV2EuBWbCbi+N+3/HR/QPe//BD3N2f8eGrVzjvO14a+HjYNuzD0veMCZ7PZwCKJbRTFiehxpiImagJdQCwl+9PjXBqWRMEoPAtu697fXGDz7y6x/sffYRP391H8SN9DgCwDQkBi/ElrOuazGKQWo9YrYLeF3KBJxIBwTrC1wHCL7QpWT92XU81dWHv6qAX0nN0Iog4Bnat09yuQPAHAmHpq5nvN4yh2w/sO1sMDtxzCufS0jQg1EWFlPc3MX9UEfO/o01FuoBIrfTv/MnY3Idq/aCoSIy+YLSGnQiDBEsnnE621kHqwmLBR68e8PLVA9q3PszUVGjYxTsn3RBzXdQa3Jtub9gIOPWOm9MJ7764xXvvvIMXtzd49/ZG60J95r1pe4la1ns1Rcv5nNar0QckCxAnsPJV553BCws4LMDNXfIj3PO5x4pbwprXOjJerXpWSeelSqmSMVLOw2sNEPtuCgSNuZFp/WeHg/relhBKn2SyqhAQMYleUyX3gSL0pjVWlq7lIaY4m2qtP4CPurHc/9aYj29Xq1YPLQ6mqbVu+nagEWXSOS0fGVS6he9wezhHhsq+bXh4OONs6bUffPQh7s8bXp0fsO+MfcvSup3S56cEuZim4AjY0LDRvAuWBtUwenPhnCg10LAvlpYEGoRq+3gstvvkejrpRm3rgtPphN4X3dLerk2W6xW1HHazDLXdzI8DQnui7k5aObCZ5YbUHeOAo/cCPtwkrbAeblIM64wTnBhTZ90zRSt7qjYuZXF3q3eBRlnp0LKGIlrLri+tWX0CUVNqM5DSUysj0oqwlmsL3lVwLX3HEIIMYMgOwTAgoZks277j1d09Pnr5Eh989BI//6338er+AR/eq+Xjbmisx3mkxaVDzeCbK1s8swwgAUfNi4i5t6qjO+sOxgoIzSphsKuzoNrkzwI8nNVEnPEJFDUJxj6AphU7F7PyuGtBFH5h8z6xgMzFFhY6S2VWEFiUro8pTR8DGbWSubfUAQvwoEyxDeMGaYxRxLbojcy9RFrRmHliln1RZjrdx7ReFzygHkuv9BTB/MP/L2kZqhnLxXJSH0/st/lX78LjQuVtgkyrxWM6r2QGNq/pgwbyXY1Fa8YwrC5P65Z5RerS6oIBwYPvEi4IyxkxYV0WnJY1hmbfzhAwNt8Pa4gF8I4QfisxHpaB8/0Z2/0Dbm9WvDytuDmdABk4rWuWXViB3npYI1xZALHu1myENFjT51t5fjG0KGLWA+eppdW6HgGgPC7C+GkvAtez9oJSnTycJSbGm+1w5O68189p3VWi5kK9ybkX17J/HZB16PP01rC2hoVUQVl7x7osWH0TP9vJuPesTXUt3sOtJXG/yfLxiwR8YAIgZcdazgql+7BN4Py7avkw8OHFwfZtx93dK/Xj39/jYdtVw71TTfdu28FDa5V5wFQU6IosD8/0IHgup9cBcGIErIxt/Bb8E85RqRAw7GuJ70vQphX60u2vdZfHZdUy0evNGjno7nZx14A0qwALCsAQ6akiumiXBbn3gbtj9Dl9d9ssN+3oyh/SLTUtfveFKO6WMMDFsZgSfSuDsIUrySzLUQFqmh/fG5gbIAwL41cB1MjSazWWpBGbFWABOsDdMlmgYPNh2/Dy1R3O246Xd3f44KOXeP+Dj/Dz73+Il/f3+PBhxyYqsKvgJuiCEfseKEzjSL/ls2vaRITVyuoP1sJn6it2F4qCim7jRaKfdyGcxx6AxV/OnD0eaiA3Geum2TAPiDA8qDP8zFXgXhDjEw/z1HdXGpV/6z3DRVC6UO8evM3WTOiCbn1y66DRoAsUGMOvmhxZf12AuLLC4hUYcl1Mlh+JXy1uSUIY1767tll11fycQvHC2C4XH6789nSL9XwAQTBg2rpCVRcYASbE4klCebJdaRt0zQjUQmLVnp06vHbIspwUKguAMcAM7EPdjMJKi0QAm06xgbF1YGyA7BvO9x33q1pDbm9W3N7cAqKgY+lL8BhlC+kyE3AW/hIBcQUe/l6Et38qxJUFxThcMAEaiBSoNWR9paAzJGkcpqoCElAV1pf4J0DKwf2ivISSJi9mO9fR5Y+2nijBi9/Xd6H1WI6ltQAa+r5M1ibfhHTaybZYPvxhjq6Y+t2btE8s+CAeAGthGmIGbPtx2YbthmoAw4qFMQ/c3581ZXbfNEth23F2DfflHbbtjA8//AgPDw94+fIOdyzYJLc2vh+p3KywiXP/edPUpSEaoU0CrC9eoJsZ1/1n7vPySVAGB3jMMwhgEkOQ+rdE/AjS9bE0i/U46Rb1Nyecbm+xnk64ubnRzInTCRE/AphWpotx3xRFyTrA/QHysFu6m6puZPEkRJ4KrH3psWFcV4sCmq4sJshQ7dpiLC3XW7UqttXHw4KdxLJqhDAWKmNg5aqbpnWyjIjfgAWnAg3UWAUzm2mBGSANctW5IHBjiGygjZXLsdYXGBDs9hpQ8PAgwMMu+F+vHvDq/h7f+tb7uLu/x/98/3189OoVPnz1Ch887HjYOawaruTK9EompHLRC6ZlVoaOW1q7gMIYyWpQWHCsp+4RALKo/tZ7ZPB4WXaxTR+W5sFgFHVtdt4hDOwimd1l9Eu96Y657g4T1QoVf1KAXrF59mchSOXf89p8C2WMHvl8/VtBRmgXNOIRc1c2jmjrCloaeDuDqWM53QLYwEx48eJdjCE4LSesXQO1WYCz7ebLwmZGJ2iibrpdrDfB5I8CTKGETGNRxV3VgMvV8qkP0quClkePq6ccrR36ZV6oaPNeSp3YAWhxaRdgQULq/gyh22AxoWpdE2AI437fsLNu5Kjp6zsIgqVlZosacyVq63RojUQi2PYVWsBxOw+8+tQDIA03pxuly+J6cT6aLjXbgZZmsBjWBy8+KOa6Fe2HDqYrQ8bzPRYIyf9iCwi3SLcWANg5Qih0BF0zZjH0SU/LoVs+bLsHmnkKyKeMnJqMBpwWDzRTX67RllaLuEZNF1Lr/dobTkvHTes4tY4XZvm4XRf9fulYWkOnhsUCbTtZ2ID9pwq5KkStUGpdM28OPT7B4EO1WDMJs+4SKpEbrnty8NjBJaj0bCb1s4GPh03dLOdtx8v7e5zPGz68u8f9w1mDCU27dUuRh3hcIFmk1kJB7jDBg+kFKugviDanRonQzJsOPipidkbhFgjbV6UtWk3Ud+/s/l0UG7O7eEwACGi2UMwcSrFTo3ZWBZ/3UJJom4KEQLniSB1lqxWDE+XhK8P1xSEGiEC+qOzwmgwfEeKecisQT4NpAMRI3WJGSGDvQ9MrO4Eagx0nNXuRphCemXG377g7n/Hh/QNevbrH+6/u8er+Dt96+Qov7+7x6v4BdwPYJK3qRQxOMRzkv9W5979twV+kBTph2Qh5+rNr5GQ39CBkMXNJBCm7PsNWzdeLyOWFIxZkCCe9utm9dTWD09QNuOvKv/R5MTF1YDHHT2XCr7QLhlSkusdx5LpCoaEZkMwrrwDBoM8WoBRtCbpf1hNON7dGixKaZXc6ceFM7nrMa188iQvzEPomMmQ66lGdNb4VJLCrdyhzeQQtFWREYONB28+BmXsQvv/wp0W9TXjdDbH4uQQjzpAafD8l3+PGAew+OGgybJvBABH8LPknIs5GPa1sihmrq3s3l/rRlSRzmrbXLPHAS30EAZGgNYkxvhyPnC82Nw7b5qRhAZEi8j0w3PiSAPDdw3XCjpMrQWPIHup4GY93/pHVT+vao0pAeYuyHuvQBr/H4fjyuRkv8ayjm6Xjti1YW8fNsmDtHael49Q7VstAiiwXKsGoML4Er63sOC6VynzaN4cfn1jwwfsDQEO3LN80eJT3YfuuCGQIhu0m++r+Qd0m5x3bGHi1bXjYdrx62PDy7g735zM++OAlHrYdL+/uMURcHtrN9M39Wb2R1q0BsInueruIptj23rFQQyeCQOuDLK0bcFi82GcUMJojhvUe/aR7vAiGLu4GUNdgLbKy58vNjcV5vMB6e4P19gWW21sspxP6yTYeW9esvVE0HoigLWb5WDQIE2sHnRf4Lr4ASrQkkrV015KRLhG45qFCvftiAUVwKqzoFpgtE8eqk5r5N3LDyRiiQMGkLa7WTwitbKirYJAGlzH1KGWO1kG8gmhBG0Nrd/QdaAA3xk4CpgYZC3YaePVyw899+AF+/n99C//zW+/j5//XB7i7e8AHH73E/cMZ73/4IYYwdqlWDoNjvQWY7OEaGgE+3OXSHCQ4mojI38IMDfN1IqytoZ86zvuOfTvj9rSgkwbTNWo49RM2VlAtmwDbCaelg4Tx0Yfv43Rzg+W0WiBwQ191rPd9gDHwsD1gZTWLU2/otOD2nXdw7k1FkRXVW1oLxkGi9Uk0HoSijkmpHWdA5XKtignTBOW2no4y+DGgEpYXtVq5i8K1RRdi/h3b9yzA+WHD2Da8c3MC0DDojE12PLDg9OJdLOsNtn0Hv3qlRd5uX+Dd9z6Nh9Zwt+9YTjdq7btZIVCg6pl18dy2DsY+SgptMdVfPGz5xZ6lKglmfJzBGSmwB6A7KSO3EIDYMwPhIkA5N27NCX4EAnRgtAEGY5gdkMBoJFgIoLGB71/h7oMPcPetDyAPG2gbaLJgZ8HOGzjo1mitucARLShm/EYguGdGE8IKwtJUUJVD0AGsG7A2vX8zubsN4LxpRteLF+8YENF9ZtZ1xenmhBe3N+ZSZCykPGoMzSgbLFhXBSK9L+rOLCjEAc22awmG7XzGw/kBH716ifv7O9zdvcL2cAaPHU20KCP9/7j7k1jbluUsGP0iM0c15yp3fapb+N6LAT3xQDIgYTpISO5Ygg4IiYZbIEEDgRsgIxDCQkbQsugYmYYFfQSiYzo0aCFAek96D/gp/GzjW51q772qWYwxMjNeIyIyc65zru85Tw/pyOPeedbaa1Zj5MjM+OKLiC9CECbHe6xxQU4JjldZG6qz5OGQDLytUS4kMSixdlhvQByANa8lh2xlIJHsMUySn5dZE4ENwOhcM0Evp6FmC2u3e4319BL2Q8qQDWBsxhFDCJi6DkPo0fnKfEz9gOAIwXv0IaCzNiD0SNjNwIf3aiMbu9bA1S8DKL6y4INTRHLcMBwaXom5SKBH7SC7rlIKucaENUtM/7Cs2M8zdscjjsuK3aJsSK6UqKVhma/hm52VFbECQKHWa6WkIkJB3BZjrvQe1GjxiRdg77PN1Kjx4nySGXMqJbBtOaw1d4NVlrhab15OVr2ywiMw4JLEUEXZPENFH6pXcmIU6vkV0NHSue0bQXWBcfPe5lWPqemTe1xxfPMTQKnX9zDZYmQxrFpZXehK0X6QTqUSryY49g3rkfAwz7g7HHD7sMPtw16TSo9Y14hFE+/a8Ipdx+NCS8s1IGZ4Kre1qso+PkjpYKpsmWPRJnDt72z6MeZZqFCTep3MGcfDHvPxqPlPCZyFBdKBVByoJZHLgjVGrcpyJ5oZ9nI255cgZayqu1LnUb1D7b35jMMnl1d+wox184LP5DqcjBGKoTCGAqincuLYc4EkhQEovT00nCVesEPf90iL6PwshwO64HF9cSmZ/IXKR2GLLDx6cm3GcBhA1uRc+/ujFzcj0v4uR4Z5zvJau3V19pMNVsUtj3ICuDya+64/S3ULF64DmbIkRDsptWarCoGwhtI/a1X15wTHgGOy1kfNeaBhhQhAOwZVC9dyuRJzBVf6MvOk25AMWFRo1nWFd4Q5eCyLtDHwXhM/23w/lrWAxtAykbb9Os1nOh00fiQ+qQUJjRyDfb7RM0nzq6COarb7RoArzkUFA0VtW8/BGIHGAIAf7TMn4Rc8OiqqffxM+WvLGso+xDX8T66ADwEdAVPfYwgdeu8xdp20behkjxB2RH53aqNcE3ZxqCxIPQcNx9jf9bu/6PGVBR9xnRHhkZaIuMRSrcKLJIutGmaZF0keXVKSqoCUcH88SuLg7iBhlmXF/hgFzevnOxC85RAoirSNtk4cARteY5++1H7XBUQEkMtqczWzmRi66uvMIMBk0y3HoqZXys2ukurarMwawGmpqzR9C7WfisVEDcE0h3MM9rnkkzjvkZwkpLoYSwy1ZHrbZqgGVspGJSveVUa2iEAxk5CtueINtjGAbe6nS6qUsulVny5MFHbFOlf6IEwN1hUprshRJZBjLGJA3nfSgydHGeZOw1QxY8kZu2XBJ3d3+PjtDT55/Ravb+5xmBfcrQvAQIBVQ3zWozzFWwSjgB0RujZN50dsECfXXSy+eauMoKPlMhe2TJKHMzwkL8YDQIr45OOPcHFxprktBUnK2GkynneEdV1xmGd00wgXPDgEvY+5eNKqSK6AgRV41HPV38qPFpJ/ZjN8dI9/N5zx+WODAkDsHpycAjWGBdUgC3MGdP0A6gFKjBQzljWicx7b8yvsbx+wu7vH29efYhoG/L5v/gR836tmj1xYTEnugXOlq3E5NzUmRNY3xmlDyvXkQk/H7PN+V8bJjHKuI2lxdGrG/4TeB+qa4cr4AA2eVHaxdu8Wo54MfBBL9VnO8E4q2yhFpPmIeb9HnhcgRgRySMQaoq1UuowF10Zl0MHPUc2pgIu+D9oVPCLqffKQMtxRdT7GzheF4BRnMDLu93thpzJjnA7o+h7j1COQVIallLEsqwBbvXATd+RH4yCqwAoAzCgyV2HJGLEo8zHPx1KQkFUWPrFcJ61SWhwYqE3v9I5m65OUSxoApyzxpCZ0JENld9mcXmtCx/o4BXctcCnhjGzOoI29XJ/TWWBukgQfGVPo0YWAqQ/oQsDlOKHvAsa+x9QJ+BhCB+8cxk5+enpUStvefWrOx86PDfAAp1ohvweqXVLMiITSeVYk0ZNWuWQsMSr4WLCsK5aYsABYYsI8L5jnBYfjjHmJmNckk0o/W+Lyyh5AkxoBoHhV5r5JwhA5gguSgOM8VXQJrZJVe/s5ytdNPoj9UidRiakXwILCdjjvi/Kp906zkJs8EGd6GySVK7YEbaPSzQjOgygAPiM6aWIGJ9ecNJm3on6cCIyZYFaRRSZUNgjVKFgfB7AtdhRQI2MgYymLqxoRG/PT8WpyJU7QgFynJVBa8ol1NPWh9lpZ5xVLjLi5u8Onb97i448/xqdvbvH29g57LbG22HGbR/CZo8F1bZlo3Rxa+PT5AMREvco12Mcw14xy9XiD8/KebEyIeiGZsbt/wH63L1UdpnUjm1OS5nWhR1oFpCcVyCvemxkmuwe6UZfz0ZGwDbYdj3L3jIV6NEYtxf0jx/JHHCdvbf5h5/CYjDG2wtiPpPMTavSsSo0y47Db4ebNG/zwu9/D2Pf49Gtfx/b8ApvLS00m1GRD58VghSDhLD2dpDS/JJkTwK6KtdkZFfBeR838jXJhJOAgNQPrC6ijmiuhT2e2vK3KfBRjxTUUA66MSvWEdU06SGUVa7VgknCS10o9VuZ4OR5FD2lZSyNOg9POnAJUACTXKewbNdfL0BJWrhL29pzT56WFRa5APDFY+7JkZixRJBD2hyPIy9+C96AseXwGJsK6ggF0g+gclT1IPSUB1WaU6z1KSZiweT5imY+Yj6JkvRyPACroIxAoZ3D2AOfiZDAL55EYAtBybUq6Jm3XYetT15rlFrWPk9whu5/NUSpHGghQ907bU9s9iAojLzkeUkLbK9Mx9AOGvsNmGDB2lushYaXea58wqj1e7L7VdVkXaKngIdkThGyWakMAJ+D9xx1fXfCRM+LKCjikpFZAyIqUMtY1Yl6WE/CxggR8LCKJfZxnzHPCkriIOwHQu1TFVYxylklbtmFlNUw63UABlQ3OgYpYYhs+OT0qKiRdiUxGH+YqwkVm8wnWodN5Yz4089udAhATK4PG3axlNABxORSAOCfeADmSTpKkJblKt4KcJPUCMEVT2RCk3M7bomkurtriU7rTNsF2AzLjxMyFQZC/N0jePo+axVYWGpWckdKxNEHdX1FC9UGDvsxIxxVzjHh7e4fXb97go48/xs3tDjf3B0TApM2aDbu9qnrf7N8luVTfVJKuyhh8BnN+Zg4Ur83+ouCDHKknLpsGs1SsOFDRGyAGdg8POOx2IqqnMtCm3IqUQU42nGVd1cuTkKSJ2JlHXDZFoIAP2y6k5Lc1Y+246Kb06EJPmIvHLMj/r8djYEqughHUTds8Wk6S35RjLN4ZUsbh4QG3b97gw+99H33o8OkPP0KOGdPmDOQJ5KW5GJxHYkgPIVPxJULiLB5xysgEcK5Kj+Y8SHWZGnhYeSOdTgxdPzGbyBs1PxvHQY/H4IMhuTC5OEfN+BQyohpZQPamzgescUXMDI6SqBz6IB1uc0ZcVsyHI9Z5RlxWbcLZACgYAGlQN6DMSsKpmbHGjHUOEFAkjBjaQyllGIFilW2AOToJx2VBdzwiqPJv7HrAZbiYNAxJWFfREOrHASABCihz4rGsHxfmL0VZEwY85uMBy7JgmWfJYdAhJcj+wurQ+CDVH5afJlp98p2lM3qSfMPSyLQp32V8FnTU0u46zc3mQOdQBR+s26R8Wg37WzKoAQ+GI4b3osMyKPgYBwEf0zgW8NGFoPlnmsNBeqfLsNXvbNe2zYYi/2eAxxn4+N13wvb4yoKPeV5BHkiL0GXrLDkfEn7JOC4rZmU+5nXFkhMOiTGnJIAkSu+WrAbfMRQHi4HOACJLglSbtX3icbHGvJQSL+EWoAANGXwqQEbuDKsUNIMpIxuQ0TfaJGILlsNSkhR8eCe116qz70KQJKfSOyZoPEgb2ymDA/OkSaCI0LFJvCwGEIT5oNBJ8pz2wMkpiRS4LQKdURYeYh0E+Q6HOggGxj8bL5fnFEKQFTGePIsWeMjXyvmnFIsBtY2DCKU2PevGmZMkIgOA86HEb2/vH3Bze4cPP/oIH378CT59fYfDGrWyidCRQ69JaTHF+v1mFDThykCGrSci8ag+T1j+Rx1txUz7G7HOKe+kWSEk6SvnjJhZekk4B69lu8txxmG3x+3rt7i4ukLXDcgxgrOBC4DHJPR36JBUSI2sLJebPa65P7UVeOXjCJ/DcPyfPvj0BE/ILwWtBTxliQMShB2Cg4YUHPppQpoXzPsdsEZ0GeBlwWG3w3/9f/w/8cE3voHz7RnC5TXOhgkzJHzoOcOnBCyLqvyShr7a3J/qpRbjkZIa/VzPF+qVaq4YUzU2sm3zieE4YY5YylmZtREeailoPgEe6vwYPtP/kN49yU1g0eBYI9K6imyBC8CasByOODw84OHuFsvhgLQs1gS67muNqZFF0BjM5iDNzo+moEsouQMeEnJcYi7rZjN0GLoOF9sthr7H5flU8pv6YQC0J1PMUrEGJyFYZ2XmIYDIY11XvRdZwtDlhKxs3InjlAX4zPOMZZlx3B9w2O9xf38vzMc8ozNlT73u7ET8z4FB5IX1sDwQuOJwrcp8HOdVpR/USVamyxoaJm4eqQUj1QEqePUk9EFlbpU9s3md170qkN332jvMq1hkCAFdEDl7ExfrNfcp6Hc4mJCmno94JwIatZoJDBWts4RTDVVqgQEAhPzFd46vLPhIMWLNGnbR9vYpJqzacnmNEesapR1ySog5ISbWttnSnVQSjrkFwTDMVgwj+DODUNe3Wm06LfeyO1FCBAWunj5fOBQFI0VEjLlsSKB6LvZZprJ32iXXFS/WeqTUDNgmVucsQ9kLKHDiCToGPLIkaDIk9ALAatk5uWaDRKkysHbx7aOUuDeXXC670LPtgLYMQ3M/TqwNFEgRkKQOP6WEzDWmbJSecw45JnB2ZUMn8kis1U7HWbQ7Hh7wsNvjcFywqJNoNeyOhJKORbbdPPs2nMLN73LRHrbBU6kE/t2O08i5Do8BTt08Etm/nbmDsoHrQieSdbDMM/YPD5g2Wxn/LBQ3xYTsBLARJEudtXFWTlKyXspNH0OllgkpRrGCRruGesc+u7mUvz02pF/kaEkOo1EINYkVaMTxFNBadZosLhBRYXE675HBAuhyFpCYEuJxxqc//BBnmy0O9w/YTmfoNxJOl6puhuMMjlE8XCt5f3ydhhEUcGQrXy+hW8DYI3IGV+gzRtt+zzqHrIQbrPcV1omZG9YDJ3uFhYjtjlUgXMuyy0NBEjKDY0KcFyzHGcfDQfKppH9B835dD2gS76EiXzBHDoUdlo825kfBi6NyYQyGVf+N04SzacST62uMQ4/tZpBPy7k0nHNadVcLg2WfMl0cBpAscR4mvqctF4jVcWRQUrG0zEhJ22qsi+R9zDOWecZ8nMF9j+ylIV4mAliASPYOlCXMbqwQa1kR56xsR0YsYZdUVFNFRwQ/IuSCyhg/At01rNE+V1/EzMUBNOfXFeYiF0bV/m7MfX2YY6NtOWBOlibx2u5ebt/j11gyas3zcJZbWO7Jjz++suBjvz9gJRbmY4lYzdOdF8SYcFxWrFEUSpecsKpgWExZ2XhBqCCASUopBcxZ+3IVSyFRxHQNCwFAwQJg3jlzbjK+bSMxQZ7qeTTwVRkD+9j64RkSS8aj7wQB1sDNEk296nuQDxANA18e7EMBHqx9VhB8ZWEAMTi6OXovbe0pdHApgfoIt0a4lOBNOdbyCZjle0gl2K2/Qduz4MccpmxKxJqZXhG+gUJ2ahQJsBT7Qi8XQ91AF128bcKWgcrdYcb93T1++PGneP3mDT69ucHdbl86LTpAOuc6p83fmkx9oAjEaYXtCbvhlL0JzsFBFg4BJx0jTwwvVcNFj19jsseOygMQ/QnoviINt6RLqCOH+XjA7c0Nfvu3fxvdMODFy+eahyBiTdLtN6MbR4zTJBvgsiJ1K1i9n7b4StaCjLGUWVuyp2zkbTK0MFdybS2YqvC9vecZX+YowI5zuY9mSQ3wWQyPuaXX5W9SYugQlygGNkek4xH5eIRPGT0T+uywLglvP/wQPTn8z+0Z3vvOAc/ej+jOz9F3PcLQi1bQfERUT9X1wjZ2XQfr72E5F5X5OD0n6TWko1REYaiAaxutjKo7Ys6izR/LwaqNCVHu0Cn4oDJmDnWckKWPzZqihjkyvGY2pmXGcb/Dm08/wZuPP8abDz/GcjjCccJ2CNJQcc2wEGZdHbontmtRtXS8AXadvyLkJjVkgURn4vx8i4vzMzx/8gTvv/MKz548wXvvvoNxGCDcE+tcnhHXBcfDXtphLEesMSM+7NB12ttKwyClIsYry+ENuFVg4r1HTNLP5ngUoPHwsMPu4QG3t7cFfIzDgC4EjL0kYIauQ+IABsNrTlXS/k4FTKZcPntdFsScsaxSxh6j2KHEwBJFf2rV0EzMqcwxKX5+fPCjnz9i3Vgo2kAGsUipe2E9AomaaRck8bT3AZ0L2nzSlYo7aj/TvrZxkMDGrqMAjWDlxibR7gMyoTAgX+T4yoKPGDOAhLRKkql5cZKAmkvLcUnuAcT7VSCgyK9SyhYaNdyuw/2jjGhzN+z9bJUF5d/NTm6MRfmVmuf0c8rDPBj5ILLvMLCijIeV3BqzUc9V29xbbgZR+TdU88OyYG1Bi3HXc1QBJiZLKkOVquYMjkn6qaSk5/HoQvT43X1barYostHSMaNiOB7PeomNFtEVOGeS8PK3Ekvl2pXSzoUZRS79/uEBd/cP2Gu34hMszhXA1Dim0Zxo7mP14koSI6HQsq3Ox+mI1L8Uw9qOjCUyw2ySeS319eaBVKEfICdhPu5u73A8HEoDNcn2YYAlgTD0PRw5BbioSaku13HX8zVPPuuOkxUEMqOGBA2p6LmWvB1UL/yxCNaXOtp99mQ8H71G5zE3cxqQOYGk8ttZKw+yJKGmdZV8hnlGWhb4nBEPR9x8+inOrp+iGzc4J4d+yuj7DgAQSHIuUk7gJCAse6flyI3zYedzAjzMwzkdNwDmB1UHRq/X8Crb2GlYqZ2fZZNox8ucIjSsShaPmuwzNBnXN2xkXiPWozBoh90Ox/0enBMcCH3wAmiWVM8JWRw5C+3hFIDIK04Dde1ld53kHlxfX+H66grvvvMK777zDp49eYKXL1+gV4EOa0A3H49YliPu7+4wz0estxExrliXFczGRXoB7CR5btIl+3QsZW8QpiYpSIiqej3Ps+QDLguWZcWyrHAkLKojASygqlydmeH0MwS0Uwn/JZ17lu8RNeQSlZWUvDk5B+klU5OGjRUrB33e/Ocf8Ts1/xWbY3uT13CIVXA5VwXDarUmlf2mZXhP1qBuBlVsrPY4840UhLEgzmzfFzy+suBjPi6ILCGXtEreR9butClJEyNBj2JonSf0Pgh1l5Mkj5EkF0aWJCHzc2WgvAowERBTASbm2NsdqB62LDGvfiLLGihvIHKS8GkfoLE4Va0+Ubkr2hV2NCCmtK4v5bRBfjoP1tbXNdziC1CBTgZ4zQcxft+MOXMpCeUgXqJfnDAqIcEnKb8lrXlHjAAsl8Uk1KGbbIbJ93I59c8COSr/+exBGnaihhawODfA8J7gXQfOuSrYLgtWzVMxz8Z6UmQA97sdPn79Gr/zgx/g009f45M3b3E4roioDmjKCZlJwa0Ze71lOmweKLSnET6WDW6aHu6xoeRqbwDU5NRHOIthSVpS3iZqrSoipBO0dx6BPDyEaSEiLPOC3d09fvC97+G9997Fcjiin8biaaSccTzO6IcRXm4TiCV8Cc4qk61gQWd00nN2bOJVrNfBBRifXKKC2M8DG/bTAOEXPQqAkzar8GS12zZ/1ZttWA9GBVLL/ij5DHGBywKeXcroQDje3uHthx/h5uNPEOcZ7z1/Dj4e8b3f+P/gOEe8/uQ1Pvj9vw+XT57gcjvBBY9h6HEgBq0JKUsIJup6T0BJBrdk5/qoc4BsDO3G2+L3BrxZliVbtQoqeADX9u56E0rOkb6+lOyqtWXWShqrTEtcKtSCl5LUhUQQaz7s8XBzi09+8CFef/Qxbl6/BmJGHzrA9XDzivv9Aott2c7HWa9IjTGjapUkFd5rD0lCJDx58gRXlxf4ye98G69evMBPfOPreP78OS4vLnA2jhJK5YzgHfouFD2b168/wf3DPY7rguX+Hjf3O4x9RN9HnG0GdJ0H4HR79LAVJmAMiNrmAQlYV8nF2O2POBwOeHtzh91uh/uHPdZlEdYiJYQQsMRe9C+GhG4NWLuoYW5qwigVfGe9V5ZoukYBGMbAZ2bMizIfa8KaMmJOTSXTjwfrZPaCK0io88z2J8mzCUToQ5BH16EP2kCu6d3iLXcGwnraZxUBpeY7QA7soDofKLlona/Mh3decq9Ic7C+4PGVBR8xSQ8V03UQbYeKIFu2w9oeZ28yYVQmYdPCQJ5xxhiYnFPdAEpy4Imj36JUeWEtNzKVN0WGFu5wFnKxPI3ydSV+WqpsTtgNy99o+yvQyYOV6WBjQ1RsjBpQUlgRhQYtmGr3RFLDZa8EZ9lQSGLlYBPJ0vAVc2lLTza4yrJZSWkZ5xZgKbAq1sz2YCK0MQkGI+tGJthJVpd4mpXtkrlRKzdijDjsD7i5fxAl0zc3eHtzh8MxYo1tLB5lwbeG1e5nw45XENL8WxK6qFZGcxVQs82hvWQ0HiE1o+NAJ0qR1rwQqj8RvGXeZ+0OqjHnlHB/d4e3r9/gkw8/wuXTJxg3E8I4wXmn4lCi/FtKMhOQ4cE5VNDAKMbNxoRZmwEWZ1uvUwfq8c8ypv9/Ah+kISR2Tu97kwirmyKrbDxzLobXyv360GkfKMacMub9AcfdHsfdHo6BznlxTkBIy4rd7S3YOZw9uUbOCddXl+inEf12QnAOU98jquOQyCFZGEQ7axcvsQCPVkxP9h/XTAkpVa5jbnW3hjHsvsiY8OlP/Z0g7AopQLHxK99txkMNIimDkwHs7u6xzDN2D/e4efMG93e3UmKaM3ptupKXrImiFVybV1zuamMr9RacEJl9J9UV59stNtOId995B5eXl/j617+OZ0+f4PmLF7g4P8c0TZrMa6wVQEnOFc5h3G7BRDg7v0BKGTe3t2U/tP1vTQqNXAZTQpYSPzAD8xoVGEDZjgV39w/Y7fe42+2x3x9wXCKSggVKGQkJjEWE+RjovDAZ5kWZzWnHQoe/lGXHxGWP0nZMWJM4zDE3bD3MFjzamG0kueZglVfZ/tSKgugatYoVy+mo2h2VPa1S6e3n1Ry3sjeag2VfWJwuKmFg07yqonHyIb8nRMbWVYRf4hpPatCldTZX8KHoi51DUvDBlpCULdO53mgDH6RG2Tbe4n3YTVJIKWESjYCrEZZBbxJBPSo1ZQmgVvaqYaByh51VE+jS1sWEot9hn+vLYjsFIpXpYANSXnIzBHTo+0zYRqtTavk1K5KHiuXIeYgksTbaymL0snlXmvHPOSM7kkQ+UtaiMT7FuHl3aqTo9FESpjIbyiifkTXnwMpbTdMhKytiFS3t983Lgtv7HT59c4MPP/4UH33yGjdvb7E/RsMv5SjefUFc0HvqLMdQFyhVr0CBBxE1rAjbuqzUO06/q1yyXqMBNAOs5j2HEBrwITFytjJiku6syTnkGHHz5g0+/uGH+N7T78p9Zsb15gzw6pEzYzkeIEq4mq/BDO4krGCGyfJd7DaxGqrqTdHnAo8vAj6+TOhFlgTBZSWAUy6glKE5Frqxl3CH/WTxvokcxi7AcUaOGceYsLu9w+7uHvv7BxFz8wFD6KTx5Lzg/u0NHg4HjOdnWJYZT6+usL28QOgD+r4DdZ2I8hFwzLloOuScquG3RzvHcq1mkde0kNzmH5f7WxwfrobADlkDLBVNCvhL+3j9d9U05BM2xvZKznLOt2/eYr/b4+72Ld68fo3bN28wH/YAZwx9h5AJMc1YXIQnUQc2/ZCThYLqQ9gzOVewPo4Dnj25xjsvX+H6+hpf++ADXF5e4hsfvI+L83M8e/ZEKkucxzIfkbKUqbpMSCx5S5kIm+0ZQt/j6u4pUmJ0n35akhrhgoKPRQw8EjpOiDmWuTgvq3bDZRyO0kzy9c0tdrsd3t4+4Hg8Yj+vRbclrwk+ZazrCucclpTRBY9urSaSmvTysg6cJsXqfTHwYbLvKbMoqWpoxprZFczZCgg1hwEBLvNAvr/WXtVZJvuQOOHWnyVYh1rnqm1q5uzpGqS6DyiYtz2YGsfakv5rOMe631Z2zv0oOevPOb664GOJiGlVwaR0Aj6kkydrgzYGe7klKVlFTMSyJiwxIeYmz8O8KUWNKWcQZwRmoaoJJ03eoO9rVTlbuXMqyQACBpwxD062bybS30nZFm4EhWQii4iZK2wJOwd29tMVMNMyHpVV8YU1IQ3DwAeZlIqsGLmUDhY2gRlwTltoE6QDpF4fCCChEB2qXgdDZJOdepfsNGm2bISC0zNOkzA/7xDWpyn2VzBgnjZzVm0COY+ownJEpBQrCgBJKeNht8f3vv8DfP8HH+KHH36Mu4cj9ktSjOalm6l6HFJBY16qoneqgnEWZrE7VKPMAhlr4ql2v7QJxRXYlOnWGnCIQU0pwTMhkIPIxXPxUGJMICIE76Rai03EBxi6Dokz9vc7fPSDH0qSZJDy4usnzyQRr+ukaeL9Dr7v4IJH4F7mQ7Z8iVxloNVLBEmeg7EWxFAZefGa2nDLjwIfdnxZ5sM8PGQBIdyyhwXYZAVNotDCxgIA6EKAC4DPGTkm7B8e8PGHH+J//V//Ha8/+RTrsuDy4gKUM25vbkRvg1m0UpYFP/id7+Lm5gYxrri4vsLz917h7OoKm4tzdNsJFDyS7hU5rhq6pTJOMgv0/ha2oF6bzBGqyaC2ZlKucwWAlIYW814do8S1Mk4GuMy7E7VJna+mSb+uCfvdruhZ/M5v/zbu7+6kvPbhAbuHB4AJ23FEdh1iZjwcZjgAnZOy0sSiL0J2fbAvsXtff3dEGPsOT55c4xtf/zq+8+3v4NWrV3jn1UtM04TzMymrDV1XvOd+7AHu4DYbCa+ua5FQ32w2GPKEy+sdDvMMOIfDLGHX3X4P7x2mcRANpBDhfRRFZBbAuizCfMSUsT/s8bDb4+NP3mC3e8Dtw06UTqOxrKpplGSuETFWXtHFjC4o48ZA50MB4OYQkiIvVhYrJUkktUTUpLYqaejQ+opVBrydK48Who1580OwCtfXnMjN616mQKPMtayMtTnvDDhB7WAneU1OP89y2whcZRsKe6I6V+Z8k4qbnZzz7wHwEWMSCWAVGWMVsGHLOM6shrO+J1tYJgntFROXkIvZfEOUsoYTwLlsIHYU+0EVEbZHLXdtGA1CAR7GPFRVU/XqHgMbdtXiaQilcPxtKKb53VqAG5gqomDUhF70YmVBKLNipRTM9XPJwkNUEkstXu2c9EsRowWUZFCWxFRqaXBwoYhljBsKukH2p0xAHema3CkTO0UVBUqx9HVos/xNwlcEuRIO+yNev36Dt29vcHt7h8O8Yo2MIQDBS3OqlFLp4cCWmEdccjiooRvFS+CTTV3ofSr/dk6MczU9mjzWXm/LGAAAk4Q2ZHbAqhWsaROpcXfkTiqDiFSAbGWs84zbmxv0fY9X77zCOE5IMaLPPUIfcOQZ67yAAXhlVYpHDK1iMO8bNXWwTeC1kAug5ZO/KwDhsgYfJ/J+kUMSdxUos+zKcu0NNaVsDT+qTiKIKqwnqMhYwmF/wO3bG/zwhz/Ew8MDckwYpxHICW/v7uVznLUPyLh5+wb74wEUHO5390iO8UTp/40n+L5D8q4ohbYbAgPaDsBwht7DhsEgPX9yYgCKdLptTIb+wGWfQJ1Whfkr36ieOimokXkl94CTOAY5Cbuzf9hhv3vAYb/Dp598gvu7Oyz7A47HI9Zlla6/fQ+mAJ+5GK/gHSJraX4BH3I1zdlWq6NOwzD0uDg/w4vnL/Dee+/hvffew/NnT9H3HawpnfOuMK/eBdWlCWVtUpI9sesHEIDNdothHEFOGjHu9vuS38C4FPHF4OF9gndRS1rFeU1Z2nDsdgc87Ha4vX/Aw26Hw7yotpGuN1VZymDkKKiOEWuOh4b5c0eFYSAiZd3E8FsfItH30NLerH3IuEk05SaE1RxWCv95K4dPgEgDLMpzdU1YWLg6Q3W+mUQ+q4NHkFwes4uyzzWhGWqSVA18PE5eLc6VzfffA+DjcDjUpkLJNn6ZvGI/hUXIzkkXU0CymJcFx8MByxIloY4kgx1BFfNsIwOVRBmfE7wtsnKPqYIHhyL0k6BNxcxgO5IEUTIg0SSdgjQHQxNc1a2W0jDCmqJMECdhFwEQAeR6ONfBuQ7kOhB1IBfgQ4/Q9XC+AzkPptrzpes6eO39ApA2UEqIKauhc6LqCJmgTA6URJQqU5LzA4CU4bL4+WtcAY5SnpwzkGWZtvlzlNRI2GSFr/kgzbYp6FkzyPXvUiZr1S2a/ETmCSdE7b/S94PoXGj+jySdZszHBb/1v/83vv+DH+K//tf/itdvbnC/OyCp3oUIFTG8JdGiLlCGbbaQO5tZFhcInXpmAcrQWB5KBnwQoFAKytS7ZfOHC46zhakAVvl1P3R6FhnBE4IPJbwz9F1Z1J0LQNeJR5IY4zjAk8MyH7C/v8cP5hnTOOLNp6/R9wMur6/x8t13wETYdAOWdcW6rpiGAS4z4ryAId4XMUtHTgiAXJallFn3fSeVBBqXJlADLow0k+sSECj3ufSO4c96P1XXoN12LVfKAfAFoInmhJQs9n2PfhzEoCRpEOecw/n5GTw5BEdYD9KfY97t8HBzg//r//X/xg+/+118+OGH6BgIfQd2AMFh2EwoZdqhR/YBx+OMw7Jgdzxg2Ez44Scf49mrF7h69gwvv/YeNhfnuHz2DEEVIsl7kPdYllmUl+MCMGvZvq5hm2c2bgqOiKMYX3LwwQOZkZZVOprGBB8CTF01M+M4H4VRDb44AHavcpS1G8gVefS7128xH494eHjA4XDE25tbGc8UMe934JTgvICE66srCQ2kjKOWs046/xIc7vZHxP0RQQGOd6KfsuRYzF3oBFCAGdtpwre++U18+ye+hf/7H/q/4cXLl7g4PwNzQowCgjkDcWUMQyeCgcrYUucRPGF0hK7vpcld8OCc0U8jps0W27MLvHl7g9dv3mJdRFzs/O4BPgT4ri/79bJGCa2ttepknmfM84y7+wfM84JljgL6nVerQgXUmQOWM7AyY8mpgOmYl5JbUXSY1GAYk5FiVjVbbemRGcsqekUFOlOzQh4B9RpiQVGUNqa+dWqsQs6Zw8gNA8GQEB1UPIxqwMhCdMX/hTryLNL7wavjRVRy0bwjDeEEeGU+SkdsZ2tbdvTfE+AjJwmJsCK2wgig/sp2l3Trb0uaMtdGYYX1YFIKzUoG5cnPYzfKod9jC+7Eqyvvp/LzBLTA/i7VK0I4KPonoPREsYn8OHmUfGUyGraiTUwtn1/+7epF27mYX285K+yRPcN5X5JHqQ6lWhi7Dv0jo9TQu+xQu6tKjNaxVsBQ9Ur5ZKBOfzt9jeXBlFHWv9Uxt3i/PVZtKHhzc4Obtze4vbnD4XBEjMmcSJSkY2NO1Cu1aWPovngcxq6UW2ueqCvhFk+2XamrQycEqQ69vsK8FBCMEWlZgxMKF5IpTo/nkX62gyz4oe9h4PL+7g7ee3z80UdIKeHi8gJhGND1A1KWKgLKkD4VSZIlszYdE40PbSxooEEoRbAyOmRqcnoUjZVGVbX9u06Sz2w/J2yIjjWRTStJGnTsCvsh9HAqLIsYOFco5bbnzTrPmA973L19i7u3b/Hpp5/i/v4OKSV03kvZsV6bC5JCHLxDDh2yC0hZvWUN7x2PR+z3e3S7B+x3O1DwGI9H+QyvNHtKWOZFRKsWAR8BDky+5CN41LUnYsNZc5xYUCzk/C1s5/QpZ3tMziWBNCvgB4CojdDm4yzrNrOUpx4X3L5+g+V4xG63w7ys0iNFdy9RB3ZIjpBchIRmIMJZeu+8dwhZpbd9NW6t5ktdp3WNOAL6vsPV5SWuruSxmSb0fSjaOSdsp81vCzfLC6R1AnPZ5xlSyRK6DsM4wnsJq0g+RwITwfmA0K1lni7LiqjVkAICpGvuuqyqDZVhOeieax5EZaFsT6DCbNmEJhYBL4lLusJUgmpytLAlWUttW5Gxynic9seqDm/ZF3Q82v3PTpGovKLuUzjN5ygOtP5eQYmeJbdZSbZuWfIRm/3NzFhlQFCLK8ypss9g6Lj8HgAfqSYblJvQ3iPBWVJjnlhVAI2ib0qG2je0Bkz3STDXO9X6bMWXffzFMGMIEGmCqHrKte19U7lino4PJWnUaeTE66Ql8vBdD9f1cF0HF3o4lVO3MlvAIWUGx4wu2OKV5nIWs+TMUqqoBpOcED6l8sSSjwyx54TkHFJ0IBMVSllZniLhVSZ/ThGUHVKxHKxJYLoYIUJZtZTYJrCFoOoICw6ik/tkJWtWttb1PcCsXqbka1jex+vXr/HmzVv81m/9Nj7++FO8eXMjOT4pFdEhu5M553ILS9dFzlLTT5AkQWVrMhiZtLzOezF4IYjqIhE6TfLMMSug5Tq+0M0YdYGWQ/9tyVz2WhC0r0RDZ7YgRBOWc1rRdQEvX7yQeHLKuH37Fne3t0g548WrV+j7Hs9evMTzl1t0XScMVaz5OyknrClhjjNSFu+SHCGovkNyNfzSkReWyldPPhYmUsqco4GBms1cwiNJ76vTa4e+rnh0BmRS1LXu4eBKVYeJSAVHGKYJBMLY9QAYwXmsy4z94YCPP/whbt+8wW/8j/+O2zdv8f3f+t9AThg3Ezr1+HaHPZASus5jnCZcXFyBQwd2Hot5pM7B9QFhM2DabOC9x+F4RCK5Th8C/NBpOFfmKCtwdyQNunrfofcdBt8hOIex60XUrhOjSmDkWVoGCCMpiqzeefixL0AkatO2seuxxBWH/UENHEvexmGPjz78CIfdHvc3tzjspDw1LyvA0oZgnDa4fPIEw9hj6Dup8s0JD7c3mA9H3N3eCkBnYQeICGsmZCYEZ31bxNgGEJYsG3Hng+Q0aM8bBqMfRpxvz/C1D97Hu++8wvX1FbbbDYZhAOtn9P2g64DRh+5EjGpdV1jOgqHSVTvFeu8xTROePHuKtzdvcXd3h93hiMPxiLd3DwA5hNAVJ+ao4CNGk7sjzWdirNnyqDSXCWq4GaWIQdYnI6PRrlDIJe9lJBJAX9hvogLYk87rZNV4xrSV3egLHowiKteG3kxzRS9N9wlJ/jcmzFPVCSLd66AMLhPAjgUBke1HslAt2b52qW3ARvO1cnoJZWRKKfyXy/n6yoKPnMU4lHIz3QFlGqBAxuoVm4dbK6gdqrZHQWcnt7/xz5sfcsNraVU9Afm3oVwJP9iJ1cRRqMgXYIbemA9lN4pDJLFCaRQnIRN6BF5OWA/ohKEqaVukbRUocaHUdDE7VyaQvd+upyBaRfrmlZhiHjsHpsaTLWJfSU4riQdOJBPQYollPPS+FK+C1b17dBtIR5UMgesIe+eLzkeOVumU1eu/x9u3b/H2zRvc3d5hnpcSU3XQ0mvLZNdkVVtYtmid/pu0rrAkmtIj0S/1UhmSY2KvkWE8zYMgaDgBpMnGOLne6lEYq3J6H0wOuTJoqEl+5iXr+Dpd+IfdDnc3N/j4ww/BCgiHzYTQBfiugw8encZrCYSURfvDvsM7j8xJk+6kmsM5rhVVltWu86l0LDa3jTXU5KRSip0rDkDx4hR8SMiPpMtr0rh6SiJpTYzeB1G91c0z51Sq3Xa7B3ltyljmI467Hb7/3d/B29ev8eb1a8yHI87OzzB0Hc42I3onG3FaFxAzxhDQDwM2mzMkkoTryNDEawcKHm4M6KcR3ThgON8i9D26vheg5oMk6+k1grkkSRMkaTemCMosAJ2VTeAgzhIzYlykPcRRzqlT3YTgvCRcxoQ5rmAIUzOvK/bHg4RkvMNhf8A8z6JPsS6IKcJUaYdhgHcO07TBME7YbLcYxx790MEzS+XLKk3Uckro+xXjuKI7LljWiLxfAIrYzVEodxYQKE6YrGvvnOS52NplwjSNODvb4sn1NS4uzjGNI/pO2JOcm+7Nuv04Yzys4idndeI8iBJAVBgE5zy6vsf5+QXOzs6x2W6VIQGWNQGkWk+6xSyrleM3FYoMsPE4VPMuLKQo907uV0ITyjjZn+pCtvVuDIlt6HaPDexYzoWZkYZcOdn72ny2dsOw/UGeb5nhR0wHqKzPGo5BCccY0OesyctlD2aYcmnbm6yADWeMB9UKyfYUUcOp1hm+bSz4446vLPhIzCWuboiYnKX8STzVSpbsPzVprmbDF3hhPUKAIvByMo72OrQTRCdXYUdc+WQDNQyo5oaCjFKhouEQF3RhBe1OawmhADvF5t7BdwN8GDTPQ8pmS+jFWdKpGlDntIW6fjZ5mIKZhZOcgiDvarinhgF0HlETQiANzTinzaAC1ixUXJGDB5A5AZFh1SouyiacglZ/kExU9ly/hy3jP9ekW2UayrItK1O9SYhXmEBY5wVZaupK4ugnH3+MH/zgB/jB976P2/s9drtjaaLktTV9zLIxC1jSHAU1ah5SWuttPMkX0R2ZJwpOAEni07yXtEZ4cthuJznzBnhAx9S0OU42E8N2Nu5lQ66vDVR1Y0pZoWFG2dmQ1gjLTQlOGtHt7x7AifEb//2/4+7mBrv7Bzx/+QLb8zOcX14i0IBBvcOslN8MAF7vdwiICUgpSh+lGMHOl7I9AkBeWnBnIk2ey2UN2ZwEEUj7ysg6kvvqdb52PhRAljQUlLJ0riYnjEYYBt3UVWBuWfBwJ6WRrz/5BMt8xH63x3w44LDb4zd/4zfw+vWn8GBMw4Cvvfc+nlxf4dWrFxisg6cXmenNZgMTzFtTFu/aeVUW9iIX7gF4JxV0WnmGYNVmkA7aa9MHiW1+yzxJaUXKkqu0rou0N++6AlLn4wHrMuP+9g45ZZHD1tLI3f2D5Gwc9gAB43aLeV2wO+wxbTbSdA1S6RXXBTlLRVc/SNOws2kjaqJXT6UlQ9dhGHv0XQCxdP8lThjHFWM/FJbxYXeQRp439/CHGbslFmbC9l7OYpaDk5L82OyTVxfneP70Cd5/7128ePYU52fCHBERogKj4MxhIliJhOwrWvZd1oEDkeqT5AzvA6bNFs+ev8CbN29xc3uH8L3vgzljWSX/xGVRVWHWRm85Iyowlmkp99x7VtYpFj/ItqKqnorCIjunziFqonABANSs+xZ8ABWcN2Nkz332qAGQ0782W4cC2fZQKFWBPbmibFpDw2rjJGtWQUgWU5hFcqE4YGgTS9HsQ7Kfl9/J7Katfn4EPv4PMh/f//738bf+1t/Cr//6r2O/3+Pb3/42fu3Xfg0/9VM/JV/OjL/39/4e/tk/+2e4ubnBT//0T+NXfuVX8J3vfOdLfQ8za98AvcySuKkKo5mKYcOJx1yNqiU2KiQtg1Y9XFLp4VyQhICJmmCaFdgyFd+8MC310dYNmDFXQKJ5HD4Yu+ELUyFlWhB2QjvUMlmnA/mdatoQnPPS7dYHVanzBfCUylW0wALlDwRhLDIzOEXtkzOX2LJ0nNQ8C1Lg4h3Avqj1xRhtKEVbJSWlbjN8JHAOkhPiUbxzKCuSIQqeDED0QXJ5cGFEpB8EaXhmWRf1xLNI7K9J+jLsJHv/zes32O8OiMsq9LV2viyhFZ9VmZKL11XyBtRzTWvUvjWnmdtEEgiQJCwHgrTXTi42CxAFSNZ/UvEmynmggkLbFFrgUbLnnfsc5kM3Fy+ezzLP8F6Si882W/jgC6t28+aNjNHdDrc3Nzi/uMCr997F5myLlDN8FyQBM0bNMVDAGhNSFCny+XiUho3lukUi2/UN/aobnEWOSdmP6q1ZNU+zVnJGpgxi0gTShHVdEeOKGBesSQTuOMWaz7FKcvHbT1/j4f4B3/+d72K/2+HmzVvpyXE4Yl1mBHJ459VLXFxc4Ce+/nWM44DNZpQ5zllyPbzH4oRJWeMK5wIoeLDT0nTvCiUtip5A4gROQIqA9XqykKDluFCz155W+ag2DWd4lhACcpbS18MRn3z8CebjEYeHnbInwGF/wLosSJwRug5PXjxHPw64vLzEtNmg63tl9BjTRqqc1nnRJotZ+nZ4j2mzVdbK67U7Ke11JAnrzkCEeMNdN2JeVnDoMewOOCbGw/GI8f4eC4QdKmuCJOxKAIYuYBwHvPfuu3j3nXdxeXmOzWaSeWP7jrrfZrQFfPLJvmnsbWGOmzXlFbxtNhucnZ/j4uIC0zSh6wcAuwpewIDOrVZMr94XBcyPjDjrdSmOLOtfmAplwM1ePAIJrDlRxQTJt5wCDVf83ubDmy//nKPwG0738Gz7ORewUJwjqowtYPuqbftmF6vzUvWNqr0sJ8VyzXiUe2b7EfCYnUH52+/2/I86vhT4ePv2LX76p38af+pP/Sn8+q//Op4/f47/9b/+F66vr8tr/vE//sf4J//kn+Cf//N/jm9+85v4u3/37+JnfuZn8N/+23/DOI5f+LvKptVQQa5hPuQiW52F06iaTBYVEmz/rv+uNDedRAI+76HvqMjWnnuEcDO07r9N/rRcEK8iYj5o7wASYSAAXErQ3Anw0AsvrIrTHA9pIuQ1u95AxiMj2IyRHZYPk9e1VI04jWPa55B52SRVOay60q2EsCQpenD2ImwFRooO7CX5VNo0e6tJrfkUyZwEHUk+1QqpITO5n1FZDsszWJYF9/d3ePv2Bm/fvMXdzQ3m4xEpMkLwj8JQANg13oZyVgpgvSPkxKJgyCaNj8ouMTQnRBUDlc7MVuFhxqeikDKvKruBMjfa5NGT0EsLPqyUrQEj9pnBO5FoXlagE0ZrM43ohwEZIsp3f3eP+SBe9TwfcX5xga7vMM8zfAjoxwHjdpTtkYRW5ywiTWkVQ7aqlkIGISlbAYZU5dj4Uo2Xl9Ftdu92KlpeBGcI+CAqHneMUbuBRsR5EQOJpOEkYJlnHA9HvP70E9zd3uJ7v/M7uL+7x8cffoS4CFi6fnKFi/NzPH/+Ak+fPsH7H7wPIgHa++NBtDm85F6tBKxgHOOCofcIwcuKJdW7ISC7GqNPqqC56ry0LAK2zduuubm38owMRNbXJEvOjhHrskii9Nu3eLi/x5tPPi3N6eZ5RowRoe8wTBOG7UZDDucYp0nAh5c5MW0m8TJT1qaCkoNBIPguCEMFp44TZDKzPOe9Qx+k0gNMcL5Dv0ZkH+C6DreHBdPdPfouIKmoo8Fo85IJoma6GUe8eP4cL148x/l2i2noldiwPCjdTMp64ZrEaYbRVeMFoLCjtgZCCJimCZtpwlZLb/u+l+8o+0fdj08gYNmfZa85TYdm+yJhMgqAKM+Uf4lTSmWOy0+qTifXoAjo0U/Un48Bx+eZam68yBq+5UeA4xG7oeNZ9JOKM2A2sr6uva72pIrl1JP6PIZWXtGctdqp/+Pg4x/9o3+EDz74AL/2a79W/vbNb36z/M7M+OVf/mX8nb/zd/Bn/syfAQD8i3/xL/Dy5Uv863/9r/EX/sJf+MLfZYGRyiokWBKozLXUQE6Z6A7SDbTzhJioIEJinGQbA5aaIRs+mhvWYhlLg5D76MqqYBYFQKsUyXZDyZq+2YKq3Wld6BBCBx8CQteDnK9KdyqYprSDMGRZSH9Jaq2a/KU8jCF1/SpIZkmOpxPFvE7F5ElUY9dlrg24mrEgSEJrzuIJxzVijRGH/Q7ruuD+4UElzhN86ND1AduzM/TjAOcBH3p0LmgeSC6TVNTx7F7oJmQTPuu95lTyOXJMRbMhRSkVTilh9/CA7333e/ju73wXH/7gB7i9vcO6JDAcXJDYblZZ/hMQ2rg2rJtpVgGt7WaqjZiapFswI6vxYZLP987Bhw6ZM5b5gGa7kWtyxmo8ZkPUCMOSufCZhyOIgE+hO10paSUihBAQQi2p9j4IU7AmQKsrNv0ohjQl3Hz6Bru7e8R1xTCN2F5eYJhGjGcjtufnGKYJF0+v4PsgpaNxUSCnpZ8OoMxY11k3+KRzS8sMmw1HmEirhMmnP7nOA+h6Ms0MUwxNKSHHBZwilpud3KokiaucMjrvsB0GbIYei/dIyyyMVYq42J7hxbPnePnyJa6urjCdbcEkybX+bFJRMemlMYPh/YDz60ukqKJS1XahUBAaQvJE8MRwWaBHYm33kKKEAXWjsHwWItl/pL1q3eST6gkBgNey+MwZ87zg9evXhbno+x5d34kE+dUVvvOTP4lpu8H2/KyIxkV1ACj4EtOXPJiEOC8ojRlB6vxAHYmg7RQYOa6Ix4i0SjdkJoeuD3gyXGOYJswJOMwz7nZ75LsHpHnRNQQpO1cl3uvLSzx/9hS/79u/D69ePcc0DgjeyWuc5EVlZTJIgYc5HwxR8m3XiHQcV+0lp8yvE4G/0HlsthMuLy/w4vlzcGbc3d1jWVbNpREwe2rcJc/lhLEGavlpmceyLxmIAEFDpzXdqn6i5V7o3X0EWND+LMCUH31GPST3xPYqA3mtca+/GNCQ860QoC2ksOR6b+wIWefsKrdew+0ooE0NWP0+Y3A/D0yYU2XgxLtSFux8+uzrf8TxpcDHv/k3/wY/8zM/gz/35/4c/v2///d477338Ff/6l/FX/pLfwkA8Fu/9Vv48MMP8af/9J8u77m8vMQf/+N/HP/hP/yHzwUfVoNtx93dXfndBqVQdAVdGuPAZbIANQlINm6WVAmuej52UPNo/6r1LW0ExwBkPQyWWykW1/d/nkUxGr6wFj7Ah07LXOW6UtEeqeDKQE6lvwqUKoBC4muuLOraBbVdGDJGTFwVYlPDOOhEtIuWjSwK8FgXrMuKeT5iWWYc9jvEKK2hu65DtwqYYoL0aqAaCxdBJncixlccjceDq/Tv6SMjrtJQzlNAThnz8Yi72zu8ef1GygmPs5ZhmyGwBW9hFvP8SJFnrglYGSBPpRzTkYgGITOyltUWsZ5SiqpAys7/89YlNSzH5z5fPYTPThebv1Q3iUfeR/C+zCU5N6HTHQhdsMRGEuOcM+5ub9EdDtgfj+jHHsN2wvW6Ynt+jvFsQgeG67gYwOp0yeZkYm/rWj0hb43wlAVi1HJmC5NZeWtuaO5SNfUIoHDDeq1xVTFBJc4YpelYp1VHBDUKzmEzSrLjdrPBNE1wQdqgZy9sEYiQ44LMCTGtIkg1DsgLAzGDIxeQBHUATlSMCZrzI96I7Q2UI8qKbe8bmbFFGU8BYHLt1owreEvI1PLnJG3b+37A2fk5Li8vcf3kGqHv4LsOrhPw4CDz2ZnjAIvJO+2Rok5F2TNsoTmQ1xCUAiaGgkMFld3QIwHYKMuwmUbc7g6FzWNI7yVAjNtmmnBxdo7rqytcnF9ongcE/Fvo0ZyqzzHPj/OimHPZB8lRwYLQ9dB1HcZxwNnZFvv9GTabEd47xFXaAnDOkgx8auvrfqPX4XVsLJ9JWMBcwugnTkLzOTWaQyd/K+CDTu1F0SAE1T82v9Gjf5ffubjeaO0SUNnUFs+XNxqT1IytgKtGYr087LaojW0AlRlH+5zHBxe7oYerv5P77Ot/1PGlwMdv/uZv4ld+5Vfw8z//8/jbf/tv4z//5/+Mv/bX/hr6vsfP/dzP4cMPPwQAvHz58uR9L1++LM89Pv7hP/yH+Pt//+9/zjNyEVkHJ+pG5UlLHTmX6gaQIMbgPTpIKVcGEJFEsS5VDZmCy5jlj6oEZKoSJimSYROr4lF5nWzuTgLEKJStCY45STIlr/kHvoPzHULXw3fysx8nqVmHxG9jjIhZmxjBg9kh6XeXKheIEUwxI1JEdrkYAJ+9CIipiFhNZmYgJ2SNuZpHX0o5Xe1smOOCHCOOO2E55vmAw36Pw36P3f4By7Lg7vYOMUXM64Ku69EPPeblKCqEwSNPG3T9BEZCQoInj5JM+pk5yTBRmmq0kuSj5IiUJakuxQwE4Hg44M2nr/HRD3+I73/3u7i/e8C6RngAa86YjzMCeQRy6AbJrQne141PhXQ4ShJqXCMce7iuL4uQIJtcH4JsQBmaE8NY5xWJIlbfXoPMGVugjxNI61RrqmE+B1BYOMOS9Kwnw0noBbIhdl0nITcn5ZvSNh1wzmPoB62W8jDt2XRcsBxnvHn7VtibPuB4POLq6TUury7gyaHvpRMy+4DgZUtwzXXmlKRRl64509nwXSgbnMmzpygVLHVnrb0sYIZBAZo3RowA7gNE5l8sgIewOZ4Jo/dYug4XZ1vE44yzaQPaCBB755138O677+Hy6grjNGFNGdlJOeQ4jghDh3U5IsYF+4cFAQkJGd3Yo6MOcZGmXxxFuIqcltUGYRqVthJp7BRLMduyLBo+kvcJKKTCcjGLgJgJgomeRkY3iCz+y1evMI0T4rIiritijLi4vMR2u8XXv/kNXF5d4eU7r/Cw3+OTt6/RjQNC12lZEZAN6DgH8gQkRqffLbogDuxdCclwXJEy4IOEbAkDXCD4WZPayaEbOjCJiNv11RWe7/Z4cyfN2KxybVbxt+044t2Xr/C1Dz7Ay+dPcXV5gd4RkBOWecVIBAoS3hSDWhMdLffCuWDDW3RWrALGwAvbPgZgmkZcXV3hnXdeYRh6STo+HHHY7/Gw32O3P+Bht8eyRqypggKva8sE3qCOiYBomY85B5iKNpSlMSCQs1TmWLPGx8yF8GI6dameL+u/T7aMz9kFf5S5zplBlBvWVMLI7hGbQnDK0smaCV672voOfZDwoncO3ln3W6vys9wcHSnHtTDh0R4G1Hn9+DkrMgBO89x+3PGlwEfOGT/1Uz+FX/qlXwIA/JE/8kfwX/7Lf8E//af/FD/3cz/3ZT6qHL/wC7+An//5ny//vru7wwcffPBZr7HE8k5vPoBCnXnvEQB0oUMC0GUxgyCRzTVdfWo/sykbs4nQMh+Pv6lgd5uI5m3r8/ZphSRrjQ1ZQqEaD/EtZRKwTjT9ftZN21QjWX8nyCZPRIVik54wSewk2g1Qe5mQqnbkmj8BWMhIvKV1PiCtC/YP91jXBcfjAYfdTgDI8aCA5KjgY9XGSQnDXjLz94c9GIRuOCJ0hIAAcirha8JVhelAQUgWi2XNbjcDZv1ccko4LBG7hwfc3d7isN9jXVfdpMQ2BHKAD5iGEdMwYrud0HWqkwKuui/MAmhSxPFwEEoMUorblp46ZUO8lY6mVBgBW/g11HYa7/zdFq6FKh4DkLb5U5u38vi1dl+N2bHEZdN6ab0dryGb7MWrS8yiOeFFRKoPHbrQodONiZlLm23BvFTneUkO5rI4bJ6eslgo42SycbYSmtUFEJXxtUz6pIyX88KWeSK4ZItBpot3cu5D18N7j6HvcbY9w3azlR4vTrvPpoyIBJcicnJAcHCuQ7eZJHzZ93DUQ9R4Aeey2XQBFyEIALGKI2ZJDofVuvHn3kfvhdYuSY85g7J48KZ2CRInYZpGpBhxdX2libcRFxcX2Gw2AuanUcYmOIS+Q6cMCHmVAletFSJX2Juke0OGsHo++Hr/LITHBCIPh64atFy5LkfCoI3TiLOzM/RdB+88AiVEluB3HwLOz85wdXGJq8tL9KErjcaYa8dpGy85NMCgIbfMDOfsb7ZHFA4BZl/t7wLKA/q+x2aaMG82ONtuEZyTogRlT+O6KlBKAmqgLRaCh+9kjrCuw+BrzlxSJVNb64xcxs4KD9ic3Uc2oV0ArSBY2cup4T6MXPhc+9JuGPIiswXypyZIY3tO47hYtV/QsOxJczkrm9XrLblpxlrQ5+xf1ZyhqBPrejB9rMrEPKZifvzxpcDHO++8gz/4B//gyd/+wB/4A/iX//JfAgBevXoFAPjoo4/wzjvvlNd89NFH+MN/+A9/7mcOw4BBS8jawwUHp7PnJCG0ufsGOqyGfOh7uMxIzsHFICJRMWJNCTSvSIlB2humsfIS40X9jvpdP2okZSNNSsUGRi3ecO0rWlqdNHdDPFfS3A3Ra4DkQiQqp5QS1z41UR7OJ0kQ1fM1cSciAmKU0l2STclo7ZSSdIuE5HJI+MB65STEZVHQcYdlPuL+7hZxWTAf99jvhPkQFUjp+JhyxppW0OLg/AFMjON8QN93mKYjUiZMm0tszzsAXv7vnKhDAhAxHLUmUK8wpSKrzSljmRfM84wcRePh9eu3+Oijj/Hd3/kubm9usC4LANEPgCf0occwnuH585d4+vQpXr58XpKbc06IcS3sxuGwx7rOePvmLfYPD/joBz9EWiNyjMgaGx27Hp0PGMMglTaphiSCE8C4JEuOzScGqFKdbfirJiY/rnJpH7ZRPGY+qvEGco4lP6fvBgQfxABBEhuheUAW2vNDBybCmBO6oUM/DXj+5Amurp/g8uwc/TSAOgdH2lujEm11HoGK6J/wgBp+sWu1eQ7RajipKGD1DJXBJEBpdIfOBzgvzarmmRHBwsIw4BnCfsWIqAnSXQEcW4z9gLPtFs+ePMOT66fo+wHkHOYUseSIY1owe4bnFf35Fj4MuLw407EOQAQ4an8RKeXSOSVCWsFJl2ACxFvPCUQsuVAxCpuQctmfSzilkUJPTiq9UohYZ2COEUxi8M8vLzCMA5wnRE3+3m63GMcRl9eXGMYRa07wXcDlkyt0wwDfBe1nJaqdZX0jI+aIQ1wEuGcR/jrrJP8lUxZWiWTP9GARoRsG6QOzJKTEWFYJPfXDgIuLC0Q4/PZ3f4C7ux3WyCBI59jt9gzvvHoHH7z/AT54/wMBH5C2BDElrDkBnGRt6CQww81wsp/kLGFxBdBi3HIxhPouEfPTw6peLi8uQQzcPr3G8TDiMPYY+w5j38Eh4XB0oLRX8Umg7zz6PmCcBHyyVuGVbtK6rnLOyKs1rNQqu7Qiee2OnhQIN5CK9DypCDjUggSTvHgcvvki9tmc4OJys1ROmR0xpycEKSfvexmDqZfQ1NAPEqr01umWCtth4cHiACj7EZzmtXmqjAgpEHvsZGQD4DLfyzj+iILizzu+FPj46Z/+afyP//E/Tv72P//n/8TXv/51AJJ8+urVK/y7f/fvCti4u7vDf/yP/xF/5a/8lS/zVbIBZ0UeWT0oIlgKELGUDTlAxbAEy5PLiOpdGUL0TsRuVqWwhEJrgUadNDbmtoEwUKQ+ChYxZkJpsZwTsiNkKxVlpzFEqpRz+TxjTOo5njxYUHvOapRTREoOMa4IZABT0A6Bi3aGMBhNrXqqTMKaVkX2SdkQSTyN64LleMRynHF/d4NlPuLh7hZxXbHMR8zzjGU+Kl0pQIkc4Fji6ikx5nkBM3B/v8O6ZsANYOowTOcSfiJCZqeTVcCH7ugApAlWygKuOMlmOh9XHI8L0ppxPC54/foNPvnkE3z88SfY7w/FczEVuc12i/ff/zo++NrX8O677+KD99/HtNlI+CYlzPOs3iqw24mWwicffYTbm1s453Dc7XHc7xHnWdU5MyhFLHCSfCntLsX77oRNsf4wDcdVdEGqQqmyA4TClLSxV/foQS0wacEMSdlzm3Nh1SfGUtSzkHlqycXsxZPfbCZJXrw8x/WTJ7i8vsKwGRG6gEVDEZFVjt3ZpymV6h2IevG8GLCeLKGT7YNQtQ1yTAVsCdFlORU1Uu5UEViocGE+eOgRskdc5R7wKvM35YR+lAZoL955ibPzM4CBoR+w3Wxxdnkhzd9Y7sk4bTB4wtYTkpfwRGYBAcjGShgjEAp1OAAAfLxJREFUYR6eiIEBWuHklZ0kApHlg6ih9F6TpwnZJzC7UpHhvdPeU20OSQWZoQtSnaIsH0Nkz60abtpsME4jNmdnGMYB4zQh5gSOiySuOyc9QxR02D6JXpgH0RmR50EOcZlBAHof4EYvPT3SCmjlTUom3GcMq+wpXeex3YxgImw3I6axQ+IEvwJpjjifBjx7coXnTy7x7OoC0nogo+9GEImQmiWZV8haDVRrfGV9NOyhhVvUCzPFzNL0jhw679B7j+00IjiUfVGaUi7ogpcE3CRiY/3QoR96TGMPH7pyX7wPCgwIKYlmTtTcmZREiC2RqNEmYjiKmlhZsr6QtTllhgETlORVYzu+COPR2Ho1+JVZtftCqJ/vgDoeLqDvOgxdh6kXTZ/ee3TeI3iHQJITVrp3s+obkbV0kH3ZwjZe1V19e6eKaJotYtIyYs0fMmzyWWroRx5fCnz8jb/xN/An/sSfwC/90i/hz//5P4//9J/+E371V38Vv/qrvyoDSIS//tf/Ov7BP/gH+M53vlNKbd9991382T/7Z7/MV8F71RLIGaxlUGWD1QnrdOM18GECWdnKFTWg7HWhORKhHWsVXSq+LASgsyWTAhLiMnkMjFSiWTOLSbyinKVjqWPxQrMmQtrGa5SiNPdSeqOpfwfMyxS2Rz5TwEeMYny81zp4UxFlZQ+STMZU0KkINxmCX+KCGFcBNZzVe5NE0sNuj+PhgNu3b3A8HrF7uBNvc14Qk3h54zgIwu57oWwJZQOcjwtizLi/e8CyJLDrEboJ5xcRzkcND5Xicq34qyOaknoVUZmeNeF4XHA4zEAGjvOMTz99jY8//hQff/IJlkUEgkq4wwHbszN84xvfwLd/n8y5b33rWzg722JZpHTxcDgU0PZwf4/D4YAffu97+PTTTzEvM+5vb3H39hYPtzdY5hmJszhuLJUfbK23QSptr6yLBRgMWOARwNA5xYxS7WMlciUEp+JARnnXNtanoZdOm2c59U6sLLcmrirI1vkWtWkhR0LvHaZpxPnFBZ48f4YnT5/i7OoC4zRJh9dlj8gZS06qIeDg9VoM9LgiIlfZG+/1VSSbUM4ZWVVpayKprBOLe3+W8SHR0QsDmBNmiJDbktcq9T5N6EOHaRhw3B+Ffu96TNMW2zOpBMn62dN2gzD06DYTdusRc1yxiwtyyhJioAxkIGQHx5qQrGBKGAxlKFtAR6mMcQgeYKfz2gNcJf+dp6Ks69hE4qiOVQhY01JAVWYWbRoWJmLcTNhsNtieb9GPI/ppxBpXpJklUE9UK8Jy1rng4TsHBMnlsufWVfrUDF2PvuvRewfHQFxEtyQui+4vtSFgVle97zxAI7q+x9l2xHbqRY7fAesy43wc8PzJFZ5fX+HZ1QXgoExcDyBjKfkvjbcmN78+dL8j695c2EAZd9bOwmwCgST2IDiP4By6IOCj89oI0kmCsYAPJ8nyMSGuCV3fYRg6TNOA0PXlXLyvjeViFPCxOoekbGmOhESAo4iYpPEaU1ansV5b5ioUVlyAzxjhaqBNtZsNbNj72uGi5t8nrINKqUOqgDrn0fuAoeswdr2Cj1DBhyMBH2ovve5BBkaKFDt03TeMpjMUZP5/bh3pDIEy9SKY8H9O4fSP/tE/in/1r/4VfuEXfgG/+Iu/iG9+85v45V/+ZfzFv/gXy2v+5t/8m9jtdvjLf/kv4+bmBn/yT/5J/Nt/+2+/lMYHAGw25/Cas5BiFMEOZrhSjFS9vKo6KsJicAF95zGEDkPosKaE3gUsccUDi1jWskZEiP99TKZMp0wKQxAvMgKktCwm7aaLEvVF5wMYXhPxcgEhshOyejkelIB19cgkMdMlikolnFd6jhHXiGVdxdMBI0ZJ7EqcMA4DpmmDuK7o+g6cVIHQwgnQfI4k+RIpJSzHo17nimU5Yo2LhnSyyDLHFcfjAceDMBz7neRS7Pc7ed+yFhT7sJ9lo7MEQ2VB4Ah9dghdxs3dEWGfcH9I2O1XPOxFZ2KcJjy5vkbfdxiGvpxvjBEpRcyHA+K64ng4YpkXHA7SGOt4PODtm7e4v3/Ab/zGb+LhYQdQh5QTlijhqH4Y8JM/+ZP42te+gT/2x/4Ynjx7iqura7AHDnHBklbxQHtJIAydR5gGnMeI8WyD5/fv4OL6Crv7B+zv7/Hxhx/i4e4Orz/6GMtxxnF/kN48wZcF6Z2EjTxpLmrUFObMQm3qkrT4WaFnzQhpMit0Qy0CcqSl3GJFQSybQekVZMbM+BUS8ToDI2YcLYDo4eXjPCEh4fbhFsPFBt2mx0oJ+3UGzwcgOKwOWINH7qUMPHiPXkTqG6E+QiGUFXjlsnWL0ZUEPm1gpxVVKYvRZ0ap/HTaJ6eW/dn+5TANE7jLGEKPOEaRVo8Jh1U0XpZ5weoBCoD3GX0AfIBU8kwjrp48RQZjzQkBsuY6juAsuhTWfZoAEQhzdo+ql+eaMnFAorKsYC8l1q6rTpgTrhUTSkiKhA0D3tSJmSBduPVzWbotx5wlAV7nhrWAX2OCT6LuuawrluNSXkMEBDi4YGtJktY5s+bOy+dL72QHFzOQZkQFv0gJlBneBU1WzWCOYMrwrF4xOeQcsfKCr737HJvB4Td/63ewxg7vPLvAN7/+Et987xm2PQPrHp4CeM3Y7e4lwTJIA7iYsjoahOws30P6i5B3mofWDJgyoVY+bOKHyFnCosuCvC4YuwAeB0x9ANKCQ15AiPCOcbYZ0QWn4daIeV4ROilvHodOf5cQHXkpzyeGlFDnJrS8rLoPLvBuRYwJvPV6j+Q8Y4qipApWeXZozo0VQtja1yogragxEVBbzeUfLcbIZYuFaaj5rPkrcNj0A8auw/X2DGPX42raYAgBUwjY9L0AECdgYlCQ752BDVVBZcBnLlILXr/DG2guqAIqDlh2A1k7RoTY3LIt4gseX1rh9Gd/9mfxsz/7sz/yeSLCL/7iL+IXf/EXv+xHnxzD0MOpURaPRRs5mbJphYVoVegIBDiJazoVEAouIcUER8AaFkjSJmQT4Bqfa8ctQyg1ARakmh6iwUEkm05J5kItF8zQbrycBaTkhJwlzonkgCgchVgLkXY29dB1XbHEWCoH1igLpyaXyQQOYLD3IAUqUG8xJxExijHiqImZUsp8xLrOOlasXoF0hV3mBcu64HhcCksQVdALCrJKMmRochK0Fw0oSW8Ft8C7BL9ExCQb65JWbOcN+i5IkylOZUtf9Dz3DzvEZcVhv8fxOOP+fofDQX7/6KOPcXd3j08/fYN1jUhZNuiUGHAOXd/j1Tvv4t333sO777+HaSvllnAk3ouWb5J3oC6IwFIQpVJyDsM4ggBhf3Z7DEOP25sbZD0vqRIQ8SapmQccsurJSGhNaFHZgCrzgbowdWY6AwfNvDXD7UyaH6R0tTUqrI0DS829Cdc9UiEkZQGdulCkOMbYtSUuyMjwnUcmSD5BjqAckIKwh/AeFIIkXMJrLlLWMCNgzbPKumsMtJV1Os9VuyVLIrXFnyR/swEdrlnL1mHUB8CJkyFCeg7HdEDMAkBiWpG9GLPkgOwA9iSVFV1AP46IKWI9JjiSnimBhSbvYTF+3SVVtbQCj5rUTmiBo3mhpB6g7gEgK5YrYQKlUvW6ROdVcGWCKxt64++S7tykiaRZcneSWig2+flmDdo8Uke4ODB1ToknG0AqZCiMKwNF2VZ6N+m4Oy3bdWZMrA9QxtXFFsQJn3z8EWIK2Gw3eHp1hquLDXpPoLwKg8RSmk/eabmzllTr5VqeXVKaXtgDruMGc/zEzeacABU3K6X7OQE5qWS9R/Ak6Xos+TjOoXTSXaYB6yqhs6CJmH2QMMTYd1VNVc/LKaskBTtZRQZJS5dlkIfeSd4gkjiuWe4/kxjj5lLqCjfDXO77aUimPlMNvT1jpcbUvNtDmIzei3M99gOmrsPU9+idk/wPfXQkybidJrQL+HA16RSAV9bGgQV8QCo5CXRiEJnJotwn58kWT35Ecn2R4yvb22UzbdARacJlkqZLqSYElTRimCdu5bGMaNn9zHCekLx4m0PsQDnjuIjhOKwJUZGfldba4CaWCZWyeCtJS3wdSGl2K2aEUqA1nwRedwQLuygKTwzNY5HStkyMnAV1L+uK4zJjXValOCWOvN8/SDLROOLy4gKbcZIGUt6jD15ATkrSsCpGLPMRMa7Y7/eSu3GsTAKAuikopW1ercSSWVpOx4R5iZjnRUCI6gAE7bTb9UMRvXLHVRYyHmAUZOg8+rHD+fkZpmnEsydPMQwjttuzkugkstoJD3cPWOcFu4cHPDzs8ObtDY6HA+Zlwf3dPZZlwW63U6paroAAvPfeB3j+4gX+0B/6Q3j58iUuLi/RjwN6bQIGEuNU6H8CFpYkQU4JxyiaD6/ee1fCBDHjxYvn2D884Jtf+zrub2/x3d/63/jkw4/ww+9+D0tcgJyx6TvVbxGKHoEkmY9bfQidROoJc2bEmGWT91aeilKaLaqTkjTmnVe1Vl9CL44InfY8acvsSiqqxn0kDEXiMTntXRM84AgcPMbNiGGS8lM/BPi+B3VeBNpSQiQA3iFBmD+gVKIXA1mztdG4O/pPfQ15B8oSpukaGray7rodE8EE/pK2d+9cjaeXskbv4RRYiCT6CvIe2QGu7+DHHskBS07Y7XdSZs/CHITQwZpEdgo8rRLi8T7ZXNnJv9v6Ou8cKNRt0zVDUeLdRWtCE/UcwCkj0lqSHC1ZtOv78vm+C2BPOCyztATwDimlWpVhYTm77yzfQNlUZLkkbRPEEMtzQCn4LOEwD3IZ2WVkJyCFVCDKOlWTYzzLV9huRuQsbey7YcCLF89xfTZi0zv0XsM03mGNK4h9Ke13lpSnjJ1IB7CwRUSwHKJWMMtyIozF8USi75i5AOQUPDhIZWMA0BEBwcNzhyF4CbFrgvzhcFQv3kmCKzKCJb2DmrZdhEwOngMyiYMRiBCY4UGI3sPNKyIkqdtlUpAGJM5wXsMvVNLQC8iSDshZnRMLrVYwoSm39d7YPNRkW8slGxwJu9F12HQ9pr7Htu8wdh2m4NE7sQmTVbORtCfpvO4FZE4Qis0sDpOOuQPgEtvJlTEy4OEaWM6AaKuYY/QlgAfwFQYfXQjonUd0CdapL5OABTOedpB6ex5AJpIeIsTwoiQFiUdKc6el70VdcF3gNIMbaLdQHVSuj8wozIdMDqtKseRV1qStDKe/C6g2QTAt50SSSSkuJSK01fMSMS8L5mXGPC8nreMzy7nP8wwwsC4rhr6H9xL3NPCR1lXCGPMBKUYBH9qv43A4YJnnuqnnsq2rB+1g7Zszi+hZTBmrhm0YomDn1iRNsGKWHJDQgYKEkMyLSTnDeYLfEQ7HA4a+x2F/0Bj9BiboJUqRCfv7HZZlxX53wPF4xH63E0ZmWYSF0TCSeIRS6eNDwOXlFZ4+fYqnT5/h4vIKXdeVJEwi6dNhORClNh1N3LUJgzgQXA/Ei3PpxkkioHR82GM5aEOzg5YHajybKSr2rbA/tyGE4i7LWOfc9M0xz09FI+ynFODLBmv9dUSzQbwdMm9XrkTwbTtxTw7ZXEmpGGoSVm3ns8RReC8liSE03rkZXv2Mz9lZTkImxXrINZN6laUbJiwX5vFZmlv86O8WFwcX5scHr/dAytPtOljZp8wyZ+3vpLkx0rXX0slRx6UE3Y2+OL1OLu/g5iV84s0+4jHEgJDcHzgqIKcwVE6q+CTRlIouBCsbx6jNE5dlKXO3hF3MqAEwSfB6Js2JsH6nA+gkVl9/mlQRsr7Oev0U2CdtC/qhw/n5FjlLz5lx6OBI2ImcJJdMQlj6xYUCaBBeYYXkvB97+GV0Wc+XFVAxiuggq4gauG2rIa+TNhEAqGr1OGbkPihjV1lCp+9BnZrCXuiCIpLeN8KOOGGZASQnIaHkCHBOWi1oMYRpcUjJwSMygGvCtYVGq9GvjKnNr3Y1GDAQ5W6PIXgMneR4tI9ek0x7bYkguTEojIeFVojqeUDHi8o3NbO+OPemgGVzTK/X5joa0P0lj68s+JjGCYP3pQ/EMjukGHEsGd3mk5QZDec73eDtU7QFNAFdr0lTXjaxmBKWdJAy3GbcbcPR5HhE9dySTtTsCKyoN2nPE6wSUyYnQknMTZ4wQfpFZNEbgcugTsq9pHdJxvE447jMOByPOGquRtZNp1PNgRACLs5vpF21hj9Cp54oa0xUpdNTlGSzbL1RmuQ/AGVDEyGuDj7IcsjgwjTNa8S8RhyXFcuaVQxNrsd5lDiqD6FQ7gJcpMROqAAWUGgUtYUWIJ4fM2M5LIgp47gwxqHDxWYsIRnL6O/7HjElpGVF3w+Ypgnf+OZP4Bvf+Aa+9a1vY5o2KllveREy+j508I+MZtIY/9B1pYzWK5C6uLgAb7d4+ew5jrs9zgZJdLy/u8PtmzeYjwdMmwkA4zg/KAPiCxXfLkhLAGWvAFaNYu4a5sNLToDzBHiRKc6OSrk2NPxGIlaA0lBKN1i5a1TAd0tSWPtssIQ+LNyTcwalBJe8JJ05B9/18D6LkTaFzIyyQbeHGcD678crtxlv3TkL0KOmkkHPxbw9UrBgCbpoXuccFWaIyJUESaAmPrNzoCiVTb4LCF2n2gcBWBdwttL4Nun39KwlOAEY1/FISBsMlByYNlZPMMOFsocwUGh9Zka2curgQY7QEeBzhg+hJNZm/Txb/1AHpOu6k+Tjk83FwIkMlJyPIAqdA4BU7JhXXc1bdsbMiIdFToDPuqxY1kXYPkfo+g5Pnj6BCIM59H2PZVlAeJBWDUTwXQ83TkrZO6HzTagLYsisaICKsdLcCBMW0+R5MET0LTMoQZnJVVjhRWTh8yq5QBwTYI+UEBSobboOyTkEVx1Aw0Qu53oukNebRx8cSUNTCCDvwAjksKYEn0icYSasFOGYsSbJk7Au60A+YdABCXiZVKVjFXlUMOJKCfspYAHqZ/RO2IvzacRmGLAdR1xuz7EZBlyfn2MIHc7GCb1zGDUh1xEhqE8TjKkt96JJOlEn2WCFkMsKn7QZ4Uk1ku4zqSjCPlrvX+L4yoKPzgd0tuGSDEQEIQVpPIUkCxY5K/iUhK6y8G1IdWMOuni7rkO/dui7Dl1Y4VOGW1OzIOrmIRNWRXCgD7b+r65kAKckt43WpM9FEAVkJniSBC8i1EzpJDkfS4qIMQn4mBcc5iPm41EMrTI7odF+SDFh13cCSJz0O7AzM68gJ6kSiGssmfHem6gZinEEqCJ7Res5M2JirBbqSlkT46Qp3No4j5EzYlrhQy6GjSG5Hva7bXM1RVgzrC0sAPGMAzyGQV53PM6FpgRQqOYAQg7AdrvFxeUFXrx4gefPn2vDra65PlsQld40bxx2/ZSK0eMo90IS39TDhAg0bc/OcHl9hWcvnmMxobV1rb4NaU+DzNIN1cTf1MuA9yfgAMpmELki+OW9V00O6QNETrqRljNWtzCxkp5eWAzvJYZt6o2FaIEZUa3m8lRKOYlI4tla0pwVhFCWcGJwXu51uc0NB2LXVE9J74+tmDK8BT20wKTN0ZLXNRSzGtqT8z9hNukz769Ur/ZxCaIZApsvarCZBKDElECaaAcnxZHUsi6FQbDfGx+0TCyFIMp+UnPdddMwbxxlDttc+7yjsGeE5p6f8BhlvPA5z9UqEQklu6z5O1AhPLYwgK7HBoDY71nP4/POUOaVSvezTWEqTITsMVHCjSkhC44Gi/LXyb2VdhEZGryWb1ZLLSxyUpYD4BgBzgI+UpYGgVH0XliTUUslRwaQMniNSDpWpiprYSeTPYCBnZJUrGu+YWa8nnNWpoqdsjBeWKukiy0Tlflgtoh03lbWg+TzUMGF4yq+Vv6GynCUajlNFB00X+Vs2gj4mCZcTBPGvsem79GHgCEEdMrYWGJp0PvnS24VoF5yndt0ssphuj6E1vGgMg/MttahZDSf1jj+P/74yoKPXqmlFBMSRcnQJgfuVF5cleiSqnspbi4DJv1O5NZKZrNs7H3usfYRQz+gWyO6zKBYl2c5bC5mAx0Fu0LiJuqBWVJqJnCOBayAIlyGJH2RbAYMYVBsoR/jgpgSDocZs3a7NK8/qqiVTBrZXB4edkXIyDvS3ACJ61ncsE6jeh1T6KS+HSgbvkwooTEySW17zIwlZawpY41JQEguTgUW++gEiXuuGcHFVqMNJkqZuXFomqNzwBCAXkFU3w9wRJgALPOK3cNBxYfqArRyRHIeF1eXeP7iBd5//3289957mFS1kjMhaaa5fWlQnRGhs7WfjQ9aueC0L4rXnJmoG5BsKN55XFxe4NmzZ3j3vfdw8/YNHnYPOKjU9DCEYngzZKPL+m+xbzK23gyil5g7k8xFr03igkp5k5NEPTtf3a1Ly/akya3eEXzXoe97TOOALohnbPc9Z2mAllRFMnIGbAzVaKQYJaQTE7KXxD5PJLLsmthdrCEUNroGjrTO9wnw+OwNN4Nlr308HxhchLeEFallukA1rqIlcPr3EIKE+mICTQreNOGxG3qU9gU5IaYIgpPcAOdRKov0Egx0OKCWwjfXV1jDsi5P6XHDJ9WI8Sno+My4VNl5C63J+KLMAQNcmXOp8pMPb5mnJpxAThoCwvalWPoSnTAfhQkVdsQkB/QDy6OWcYtqtB2OnLwvJWTnkJLMJ8QVXiv/iKHzWE6XnYjfZbb0RrIrl2TNbGGcDE5cu/4p85HjirTG0lAQOaEnh0gi8rjGhDQvhVFNSa5Rdl4dW2U85EYR4Bi1Z5aBavndEyEpe0meERggnxEZyHpvZB8XJiFlrc7hXMC7EYehuWPUzIWW7QiAJodKFaUk1Uovo3EY0XcdzjcTpnHEdpywHaSkdjuM6LzHqMJ4gdQekNke+dwC3hlo3YvTSVmdxhqMIR1HV/u2FJzW8GhONat+L4CPoe8wBqHOkpcBzSFL8k+M8I4Q1xXr4rDGKN5NjmUg5NBKhFzwmiQSaZ342PWIGXBzAiWoR6uvrEBYy3BdZT1Yah5qGSLASUIriQGXMkIiOJfgQ9Ib73WRM5YsqqNzXBFVK2ONK5Z1LaVeRVGS64bvnVC2XrufGisSvKBdcqiUm7fmX4QlakWKoVyLzUK9CpAoI6aEwzxjWVcJuawSDsn5tBLImF8iCU0ZyLCKIZE0kI2/3aFtmecMLHPCumQQrcWHZGWxvPMiDZ0FDMSYEEKH7XbEixcv8f4HH+DFi5e4urpGXKUfzjAMcFolwGpNXOndUHN0MtemekSE0HWIYKQEMf7MOD7sJau+73D99Cm+BeDN60+xLAs+WWbx9Mg8FN2MPaogktMSWQWpdh9kjKzSRsqnh2FA6PvCbgEoOQyWrEwApnFE5wOGvsc4DBj7AZtpQGe5NySbSlbWLGZZE0tc5b73HabNCB8sIZbBKYKTaCM4F8r1mxEuoYmGVfncxLIWKPjK2piEXhtfNlBle59jKcO15Nm8Jk2cVNbMebD2UjL77ZwALlFHllbzm80WIXRot3TbDKVyzQyJR+gCeNXquSYUyThthKc0TgUOjMJoFINm16ovySzetnjDXPcS/T0jl9J8LuMjn2T5Mabp470rf2NL4tRvrN4q2yYBi+db2M55ZUEygyyckWvuGhkrB4blRYAIlIAQHMAByxoFROmaNxl1y0EIzhXtJM4HZK9r10fpoquhV9K9y/qrSBNKTXAlr3HuJOeZWcRIMisQyUBcgTWBkpQhM0PC8j5gcB6JHCJLea8xv7bm7fsoSXWg81JyLfmUXFiZckNlEUrJsaZhJU8CPgBwcAjsQcnD54AVqsibEnwU1rHt7dqKB8q+oYAeEhJxJNUrwXtxyvT3zWZEFzoMvSgub8ZBczyklDY4j96rJo/2xkpkvaEsR6g5D8jeAuuh1K5Nne8VctdgTAGR9i7SuZrJqLBH6OqLHV9Z8NEHC7vItTkGkhNBDuu5sehGxpDY4Kr5DaSrz/Y5ZDGwrFtGLb/y6LLmLEjGFMzMGvAwJ8h0RGr4RbxY8W4UpLCCj8xIeYVzQnEbsWZezhwXrDlhiStiysJ2JGlfn1QkKOkJtGjcuaRGVT0/J7R7CL70lei09TvgIH3VSPQEOGoWe5NQyFwYilU7yM5rLKzHGjOWdGppytZudkTHKOUK1BzsHKudIf27hkWlmVeu4wxS2lFZAu+lBRgg4SByDuM44fLyGs+fv8Dl5RU2my1SyvBBxJpgoaecdZzkJBkMY3jNyEDngfMeiELJdiqnHXXjG4LH2fkZ+hBw9eQab96+wZtPPwa3fWCaeEc2moKM7bJwQY3Ve5YkvmEcsdluME0bdMNQwwiEqhaq+QxEhO2Z9C+ZemlHMA4jtgV82DLOJUcqZgGx8zzLHhE8+qHX0ITM8NJTJyUQCJ5DvVe6cVnlzkmFSrvBGEuCR6/TVVTo5WacCqtjSansTpohGvAwisVpArHNJCKnrel7dH2PrusxDCOcEx2GukZVyZgar8wZE1bZDAMXNj/sPMri12upL6qbs1091w+oDE3ze2V0jOmo32UMQ4mvu/pQrly+h1pY15xne37lPkC0NAyMiwyyJLmyABCZMvI3ylwSZB0BrE6NdKm3JFtS56YCEO8kjGzhEngxygiSnG05P8Z8UHCQaj+VVLfr5iwaJIkFqCVh5DiyfP4aa35Hlu61gQgdOXQalg0g7YjM2kZD7x9J/gkpW+61ksc1exO389iYNjhZO07KUC3skr2H0+ob5z08BHAmkuswob+ybzeMZq06kXncaflr33UIwWMcewQvkulnZxv0XVf664yDgJDe5P/JIUBFDpWRY+h4qj7pZ7GAOaGPrhfQHLH2tazrDQqADYEYoKyAldu1/QWPryz4GJxk7yZVVkgkyonBiypfFwKWZcHSdWq8E3h/kJvuNIcBQjvnXOnFrF5vAKEjj07LqgDbKh1ItUKlPoUAFn0JAmOFLEbPhOB1kmriXGYAKSoylNiayT7Jd4thmeOMmKUHgiiOptJ4KpfNzTZ7OzeUxSK15izSvo4QU1Lmg7A6CdHELknZZhdATkp7Q5AaeHLibWYtr805Y1lXAR9L1P4MLBQjTkGtjYuBCG6eE3ZGMMDhyKWsK2ikylqCOzKjgyJj3WmPEkdOElm9w+FwEArXe5xfXOD9r30N3/zWT+Db3/42rq6vMW4mzPOslSFOQbjkRNjgneQ/gAQw6rimnDDvd9KJNSUMXS/1/po/lMlj2E44v7rA13/im3DeIa4zdnf3ONzdKaMm5dwCBkXfIGkMO8UIKWmUqppxHPG1r38d27Mtrq6vcXFxge32TJRjnSvKl6s21hN1WhF7852wXF2Q5mqdD1KZ45001hO3FpSlLYHPnYCkvitMkMmh22HgBssK6gmuA4LzIuCFL7qPFNOr42obd4ssdQ5z9bRI2Sj7d9FyaP7WfCis+ktsbS75MuM4YhwnTOOEzIzd7R1iTuqxaldaUAHezCx7BZvuBVfj0wIPmJdXr+GU/dPrOLX5J0cbJjq9nKry2j5vsNiAZ4yx5AYVH1YBTBl2fa11ioVWv1XlUJR1wTrHZDpwuT1k56meSI4qRAaCz+LhOi9KvF2jCuozQDFjjTMAh64fhfb0XhgM7yUkSwTnAsgTXHIgkvybjCxhAa/hwHUBRzn/vIq68LpEpLhinY+Iq+g1Icl6XXd7pOMRLib0cIDvkQZJml5cpwDADChJh1+GaqWIjkzZuMrNqT2VigPgHLLPmHxAThlLCCKPMAxF7n61fTTFwgIbRWzAW/KzHLoQqvaIN8VW2fNC5zVvw1eGW53MLsg98M6rVDohoJa/k84PJ56wGIzCbj0+2jDeKeYgmzglgb/aAVsTXMYKlfloHYwvcHxlwQcpIoTjoiKYdcNyILCJRUFurlfV0pQlQzexNOIpmfGtl1NibpV2BbTmm81zalgPskoORiIuwj3kGhEcRvHYGEoXkgAXmwuGiJe4IHKS8yubx+McCXu//st+Ve/M1ow8LX1kKEulhNNust6J0XVe8iXgnGSj62dYpUDWHI+UJck0JdFJ4WZiUj0rtNEU+2lxRQMfpacNV3bExtkWBJEmWCl7UwS2vKtUJQQ0jdOEq6srXF9d4er6Gn0vHkIMUsEki0HoE7LvI/t+LiWushFobjuTXidQ5DcV0TPkuUAi7DSdbXF+dYnt2RlyjDje3ev8QREDcioUJfF2OQkHkevuux7TOOLq8gpn52e4uhLwsdlupVU6STgxa6hEGqpFdFFUWqkTKtX5uhmRyu0XgpTrNYoqKsFDPW6YwdcNyWYqS/VY0KRoa9Ne1Taa+XhyH+vfWrBAZH/nE1xiwKMFICefZAbwBJzYApTPEYNdZcCtWgYkxtGqlyhJlRyjhjLscyW00iZF2284rbTRf8sia/I7lAFojRaB6zz/zJW17ET1tEnNf70nj8Yi1+7VqvaGuuDr2mrZljJWKMPSnJCeHXM930d7ogGQtnzXCPpO2Y6OfPl+xwzK1n4gAy4250CaiG3MB0vjTC+eCCtAZUgiucgFLEirSr6vIom/zmvpNRXXiBQzYGrOy4q8RlAS+f7OewQiZNfIIjTzLanSLkoY5HOa3T8GH2UM5J5nx/DMiJpfIXu65oKwKOvKEJzOO0D68Hjn0Heh6CT1QcGHygT4oIJgukcBXITBWjn0AAUmqOxYYSTazdrA16O5abeonZd1jtcEU2M1CO16QflOApXXPb7eH3d8ZcFHcMIsiLoptLxbUF0mgkOHPnikodf27lK2ljTjOqaIJUUclhlrZHDMagwyKCt6jhE5rpXqVOABrl5OZP1uMBxpTgJnQaJZvPZKrSrAACMn+XdiLuI/GaqjofHeRn/pNOkLgM2MsqEJLyYgTN/oNFqUgVK6Ja3hGUda4bSjYTcMCF2HkGu4IKsHWDrnpiiaI2ss5bZgRnCVtdGw8WcUdAmA8wHeaxKg9qUx45D1QlISsJeb90FVCh1pN1YvWibMjGWVUNHl9hwvXrzE7//9vx8/8a1v4YMPPsA0TVJ+vJmKqJSFOywRzPI/ZMORAfPqyoquAsHlAMei9SFyzww/jUBccdgfETlijQum8y2ev/sKT37wfRCAh09eI0cYzSEbVmcbQZaqCu80r2PE5ZNrnJ2f4b1X72Cz3eL8/BzjNKEfh6KxYlVQwXnpd6KAmIkRphFwADuL+0MaKILhWKt3Cr8DFR5juE423JQzMjEiRzjI9bK1BFgTQideYBccmKtBZPWqLH8BZVY+mgPFuJvAFhf8YbPYmA5mNGtGNs2UJDkyOLm6FK2vUZSPyijjktaEdREBPGkU5nB+cV4MuVWZZM5SFq/gIRu7EiN8FsYzU+PR2dWRoRB9xjXIv3EITlbACYD40YwHQXFEzjpaj8YCAFNGIkjyZQiSaJ9RPMuSt8UNiDB3m7k4FwyrGstanSKOE2cuIUqRLs+aXyGhDsucAUGTLgnboYcnj0CuqDGbOqZfFMzFGew94COy5njI9VRjXKh6QM6NBbTnnKTtwxqR11SqWtZZ+lCJeGJt8gjOYGUsaYkYiDAGVS4FsHSl01VxUCXUKwBB7keVRKj9toQtK/u6Y6QswNYrYxw1Hy0lrV4kKKOq5dzQf6tdsVww8jUfzx6dVaw5k+nn0isqJ6n4sXwhJIYnhsuMzgsQ6TTHyoWqvZF1bkGrKwvKqFOm2YPtHnF9mSfzpipo1+dyHVBY3kxRi0X9qi9yfGXBRykfQ02cyZnhnFiS0n6cGYyAlBmLhgwya2Y1gOAisstVsTHLPUHKUp6VjIY0X+TRpmEbl1h4SLsuV/aaE/KKzVBbdrn2N1CWgQ2YoOaJoHn/Z7b3snoMDQmjUUCLFDI0pA6Xxj5EDGe9BGjFmjK8NnojUjChyVlWESA9ZnIR9GI9yTJRya6Z2vks/yXxwNusepv3nQ+C+PsO1kjNcgq8s3CC5IQzQ5JutUQ4dB22Z2e4uLzE06dPcXFxgWmz0TFN6IJIpMeYVF20JlMZi3WSREh1rAAt2dQ/x2WV8lMvJdVwUmWypAXDNAJEuLi6xHo8wodOsvRNKRE27lq5oYzOtNlgs9ni+voaZ+fnODs7E9ChLdLJkST9slbqaHjBRLXQefEPOy9KjyYSJCtCp0dj6BknolIOIszFKYGCnVcNQ0CBslQJVFjZshOmw8Enk/J0PE9/5/pv+/URo9F6/DZvmVmoeagQnbIyRvuZxghnERNbY8Q4HhB8B9Og6IJofHQhqOCTsQRcM/FVVt3EwFoZJYVJ5eTZmI92PB5drjE2bIxCeQ03n1fzaGDMBlv7Bjwae7l+hjouKavRZjVq6s3a2DLgSsIokNmMe/mPyIDDEk3VzipgcQ3rAWUyyr1ZhWWgjjWUzMhrRJxnwGsF1SoAMZHorbCGUC38Zk4ZUI2Y7KmSg+EgBnqNq4RGkrQ0kFCMdvae11Lea4nrrBnunppmheZ8ZANgaktIk5abOJnpEwHq0Bm7RBY6RHk/k2RRsCWoOun+CgUZVvKXWEA8GwNp7AHK7SlaPZ5ZNEfsZtmNUfDaOWmxkGFhfVYVUnHi7OF0HpvRKNMcn52z7Xrl9jfbI8lWAgqbUe9Zy3woWFLgYeujXSc/7vjKgg8TFoJjcHZIIGSSJEVHrEmVEJTqTORKBHpiSvDrAoYwIJwzsjNPmOA1+5ujsB+FNSBU8T09zKuIUBl2peq9bow21qRIsIAPnfwxaxlm5rIRPmYOync1j9yehc7LdHJmQNDFYgCEGcgJdRISIwdGWqwKqHpfYNJNj8v3MWuFG2qMr0xkqov4hGYDyiuZBQQYj+PUEI+Tloqdn6MLmkBFqvNBli/hi+bJ4XAQsSiN6T979gyvXr3CB1/7Gp4+f4az8zPc3t6Cc8RZuESCMtNqtGUDlhbxnKH5OtWciJGwzHdvA4bjMiOuM7abScoxjxIKWWPE5cUlLq+u8OKdV+CU8d1xwAoUUFkNknQgDiEg9B0ur65weXWF97/2Ac7Pz/Hk6VNhoay/BAFRwVZSo+yDJJyRIwzajPDIi9wjp94mA3Ci8+LKxDFFs1MQwcxAighBROF8EDl3R9pXKNUck1Y3oh610uRHHqVTcfuuOm9OQzOfBSBWIumdl/uitHdcV0B1ISwdPMeE+XDEw34nImKaTBm8wziO6IYB0zhhTQlRPeUKQKlObFv0CvqYm5CrgRHmyjKg6hwU4NFeMNdk4cfwo9DYkDVXEstTat4jRwhB2F0iAQMpAVkNosb4yCoNSPuLsKw1AUryeRbG5QZ4KGlQHOOcoUJeCkJjRl7U0KeMtJ9Fndn3Im0OYD0ccNzvRW+InLQsYClvtWRgO0Szx/JsNMyn4eakm40AiVzGRBhq26egpbZaGQMUzSYhTgje6XzWOQ3INZ2oG5Mw6cyVOSylzBlg16zhuqnLvFTwoUFVkUuAABBSgCB5ZlS5R0JJIpceWISco16nVvYAQBbmJLVTUuUjtmdn6EIn0hJWIq9zz2k5s+ParNAgdBsmMhDVzteWZf/sslawVN6nY/TotQI8qD70+d8TpbZSveF08kiJmiMHb3XrhUnIJdciBAfnOvgs7a2dlxyEJQR0i1SWzKuUSgZViJSeCNLzw/I/CJZDY4MqiETCBxo6aF/rXEF8WfNMjPkwxJs1D0AKQ1H81oKu9Tst5qhscaVkG6/KNnXbKFNbkVIcIGE4mJoST80/cWpMkgGWXEMhtjcH5wqIKR6V0brEJeFPjFRlpyTU49B1XkrEug7Xl5eYxgnPnj3F2fYMV5eXmLR2XcSAMubjjN1+h5ubG7x58xr39w9gMDbThK997QO8fPkC02YEgbEss1SygDCvK9gR2El/mpxiXWlO/Aw4ZT9yNS1Zd2Fp2qepxp7guRODlbOUZHpCTg7HdQanjGEacX51ieevXuH2zRu8PhyKd3RxcSaVKJsNXJCEspfvvIMnT57g/PoS/TAgUkbmiBjV4BEQkaTjsZbAxhwBjiAQEgkjspAmQTtTmyGrYoRni8nqHGYNweh8MZYImUBxhescYB4VWRlwxvF4kFwaK3O2g7Wyo1oy2VzJNUbV5qdVgVhugb5Y2YH6mdliqQCzUtoZi8X61wXMWXRSTPZbywkJDO9FfMkxIy4L3nz6KYZxQOhHdNp6gNSBmeKANUbMaVUV4IhA5rRWYOTYZLCbfAH1yhmoORjNZVD5r+UO2Y4h4R9WMGg9VwRIpHItaY1gVoOk69pxFlE4J1bJpPZbDZjWay/7g90zlhyMUIcXKebapI5ZGN8sDAPWpJ6bCvqsMv+RsvT5SIzjw6546OsakZcVkVTUy9jWUtG3lBYOBijKfs2mb1L3cJNKr3sbIZArewqgJcNsYy6sT9ZE62xkp5b8KgpUZgAopcj6lCtzmAvhUJ0sAybyElPIZhXuAjPYe2XrjOWW0KUYXwMcXnJiiIAkzzkNtwtAEgbLw+agYkkGAA8iXxjizjkpFW+cHLFP8t3EVJgmQgsAzHao86D7TZ2ntWKFWq+7hF2aPDi1KSaK57SKwCqyDOS49KNc688eX1nwIU26dNPJ4gllyGZkWczJ6uk1vCDol6UjpxcPlADt+SGS6oSMuK6SUWxo1uKfqBNRpKxt86522H63CDtpkp8cLImumnQkH1jL/Cx3IpAW9dpegeLEiNKlcwo+1Btho2e5fKZNHm7mTBm3AtxtRusmiGYx6zVYWb2BIE/GOlm2d12QiXMZB8lfdaVRlnnLIQSMQ49pGrDdbjGOI54/fYaz7RbvvnoH19fXePniBS7PLzCNo7RIXxbc3d3i5uYGH338Ibo+oB96MDM2mw3eefcVnj57gnEcAEhXXiujXeIq2fV9h7jGIpdPJPknVlqZtcbfDCLDvDAVM2LWUAeQ4ioGsfNAJmRPmA9HrPOCfhxwdnGOp8+fYV0WfPThR+rlEMbzLbabLS6vr2Tz8R7PXr3A06dP4XtR3IzEIFZamZr54Sxkz6U6i5mRBCKILLx6tEVi3e4xLMlM7iIZuGo2+Ky0GKUVHfeCB3TmOE/InLEsswAPrrG8E60AbgycblxqFxtM8gh46Oe03lf5LAWAxCwlm+ywLEtpV84KSqA5V2uW0mBARNH6TnRJclxxd/MW47TB0xdjKQElF+ABDKvooKym/psSXJCurkWzQM/dFCrFk7QwBT3qh0RlzRXcVcrCuAC+2npSwywKak3TglMCp1jKo+2zpd05S+UIEThFEFcHh6qVqs5J2ZjE/AtANSeawVGcItJwhoCPLGAjNuCjKWfllEX+OzPm3QElL4klHBR1/7Ux6jT81QKOltmxR+YK5uyc2wogK+N1VOef5XIZSDEjzkTaUVYAlcx328epmWoGkKBM0aONE+13tewHZD0A2ibCmBphs9cYpZFp0j1fG0J654qTkEoVV50/xkqY3JrTxGhnjhxpdQwAWPKriuq1LJkA4pogXnNq6rqr+7stUrMLLfCpc8tAeTVKBmp0nyESllkBSq1w00aWX/D4yoKPZtnKvxvDS4BITBOVevVKp9Ukn5QSutBhjSuGfkVMEX0IQu2mjMhCiT1gjz4mkFbLpJzgGZoc1AisZBF94pSRUkWrulNpCauFHfRNJiKDUJF/zFApChCkPNWUS0PXS4mgKsZFi31mSQLN2RaqivyEgL4TIZoQrA+EVTPIXFtjRMw1jlsAjW7EkmCqvWHWtdk0ZFw7LdGVZliSkT0MI6ZxxDCO6LqAaZzQhQ6bzaTVHBd4/vw5zs/P8f6772Gz2eDy4qKEXYLR/hrjffL0Gg8PD3jy9BovX77Ew8MDYozo+x4ffO1ruL6+1jyJTuOrOj+SlCOGrkN2BMQoRhFigHIWvSIZbC5y/Q4dckpY0tq6OcLwwOsGLV6pUKQZzgH90COfbfDk1XMc44Kzmzelz82LD97DxcUF3n3vXYTQIXQB03aLfhjwsN+Bc8Rmsy0VGFI1IYbkxHBzpaglVs+ISDBzZhuriqfCw7X7SUk4BLMY8yyJpZSpGLqUEpxmFpJt05mRVmFchr6vm4rmJ9QtDWqAzNtrwybtIn60dtUoFEPUMB+6TcLuLEFkm7Pdaa6Nw3rv4FiBRRfQdUGbaZHqRcjDNlbnxIPtfRA1XweAE9Y1w/sKnAWQ2qpBM6CyqTtu+qvoOFcQVZmOk67RLAY/K9DgNVZ2of1pYQlIaJd8RoYDU0TW0ktyBPigoK8awvr9cu5UEtp1DueEtEpVW1KmMadU1l5ak/ZPkVLWdZ5LUmpeczHqYJYqPjN0xarJz0Q6FlLupveMT9orlMRE5ib/wtYDqrS4oOPyHbIu0Mz/JjRdgIVUiVRNmPLUSTighOyNFQDKnkGW+Am7NLmvYFY2xp6R8+uCV/l+3VMB1UHxxUB33us7dJUbqeC0uqyArArWAcA15erEloBueSf1gspMoNOEUGM9ThhHYyqczXsPlGZzqCyH2rO6DhR4WBKq5Yy1jBya93yB4ysLPgDAEjDYdi2gbgaZhFFnV28sywRy3oMpy0RkLqWTPvoSW1zGEccoMlbbmIAlYmZIw7mcBLXqhlcSXrWEKz1Cn+JtyuSh5l4byIQaPIYkvK7FO5QJ6IMwBl3XoeuG2uMDVbEv51wqUcw7GLqAru8wjhM244ih79APgyQUNh7YEqNWs1hsVkNDOZXusssqfRMOxwNiFBBCSi122vI9eOmf4RVsTOOEzVYUJs+35xiGHufn57i4OMfV9SVevnqFy4sLvP/uexjHEZvNRjfs6tmzF0+q74OGUqTj59nZGWKKCCHg/Pwc0zSdCHHZIVLOFclZomfpCWSCYNCkt+rQ1M1b/0AElfqWn8kMSk7qnQmy77qAYTPpY4Nh7NEPPTYX59hcnOPs8lKk07sOvpMx44NulloeKwNhMs21ZFyMRzVotuG6nIs5NvBhVaSuzDHUTcRULMt48YlhNEMplC2KB2uGyZigZng+5zBwUQGFMQbl2ZatM8/LdtT65vpvTegzoFM/qQFdtvlpybGFT61iI6eIvEZlNqAgx5qJcZFQZ05gFk/NKhLMoNX7YefeeIlo/myMyAkDkU/AlYVeilFOWWKeqf09V7AWtWmX175DxCCv7C9TYf3KXWi/G9DvzwJiYwLniLxm5GQNKHMBHlkBBycBHzklGbsGfJzoALSMgd1v/clWdVXmruXGVG++ZRSqoW0MKTVPNcAGjwzzyWH3xFjA5jPNdrTvPC31ljyNk8Z9nxnX6txWloDKdbksFYZFXEw1RCwPsF08tucL+JBqzooP6txpz8Ec7JadMCdc3wWTdGhtzsmgohkjAwv0KOxigNbV99l7ag+i5j7a30roBVWC/QscX1nwUUp/7KfGQArKc4+zehlplbSd4ElKvtiJt9oFDF2PlDM244CzacL5NOH8csZuWeDf3ODhOGPzcI/j8Yj7B8Yak/RXWaMaNUhbc6/bF9UwRJEy967R7tC4plyNXRUAyZlwDug6SS4cxk0RSxrHjTTFCtJWPeeqdJlSDS95ld+dpgnnZ+e4uDjDNE3YTBvRychiOCWRUaopjNVYlhXM+eRzD4cDlnXBzdsbzPOM3e6hJO9uN1sBOcMkxrfvMI0jpnGDs/MthnHEs6fPsZkmXD+9xtmZlJJeXF5gnCZcnJ1LSAyEFFfENYM5VUOoQKQfBjx7/hxX15fauViUN7u+QwgdGAkpRyBxqdbIyMhpwbp/gCa/YzNOwgIFjxQdOK5YIst3qxfjzEhEkY0jcIkTF0p8XZBjRFwXqRZhRtc5cA5wY4/h4gyXr57h+voK2+0Wl5dXGMcBuQNSB/iekBwjUYKbOgCM6AUoiVy1ABDJGIWW6/HJfmuPznaEXEvqnG5SmipSpUp0rhFY2QCpHGEWCjzFFWkldJ3Q+fCuGO8IRk4rgndShkykBkBAnhlvST5uqgWcGB9Xvr34DmWdupO1YABDPtvOt6TpeDnvwsxDNkfnCC4E6RTtpSNsmmcs5JCXBR6Mbhiw7A+ibucc5riKBkOUIFZGEgzoCaXBHOgRCKpGrDgTxWFuAGLOstasQkNzOWQy2s9cWY5VwhoURSqconQZLuCDGcwrsvdICjQ8SVt3dgT2CblUw8g8yNlAsuw5pKxdWuYCemJUBjUmDQGpmFeSUltmVtCTgZjV8WA4rWyl3Br2Cjjkzsj6jctawYjdc6XxveVCwDVGzfCF5Sex7gU4AW4nILY9vIR8vd4vJuAxgLD7dXIUocEmh6ENHzSMnbGGkrRd83JI2Q07Px8rmyzTqIIB61tU3uug8ufQppC2EqqGDWcubKMxQSUVoQVIDYgvTgB91kmza7X3O2tG6SprWu5Z8WpqzkfzpIANo13tfOw1vxfAR60d1i2Adce254sXpUiQIUI25d9KZasH7BTtkQ+gXhZ5dlLKebWsQtsTcOg6BADHecGyriX2LHFNRaGNeiBD+s6Qq5K5VpXAQIlvmvPDxAKOQKLj33XYbDYYp0l/btF1fRGesgmdsgmmoQjUbLcbbDZTYRs204RpmjROXkGLgCADH0kSxnI+eRwOe8zzgrHvFYBNSFoBsT2TcxqHET54hD5g7EeM44jt2RbDMODpk2tsNhOePH1SdSw2U2meZvQ9OcvFCXA5SyOzbLoosghDJ83WLNHPqMmUxZsjZbgK8mfZ/FnH3zmpnulCgAMQvUfSBGPWm2HGg9TgSHJa1mTiLMlidtNSUvli1tJcmQtdJ/fg8vIS5+fn6McBIUhuEesEsQ3DeVcE6eyQ1+TGQjf9PmB/ltd7EoDAZW4r0wYUj17E7xSKm0EvhIIxYdr/JYqWArwvn0tq4AEJDRKSPG9rSnOwClveXEsxRrbn2fpj9ahaA9AwO8VTtgCL5d+cjIO9tj5XStztc/T+x2XR/UNkvi0Em4tegtbaG71u44M25FKBSAEeXFkoKFDTBV7O+TMPBdakYSABKsIkUPtgwGVLgubSq4DJSemqOl6iUAoQ5apFkpu8nqwp7Ao+4jxrVrmCjcwS/lFgIUAp12Q0vUaPajjlBrR7no2N3hmWGZcLiDsFlyWEUmbmiU2U+28Gt2VwTpicx+BB/uOsxFP3h7a0tf2Oz8KWx1TEqQGtOT1WKSN1VkXttnk9Ecm80JKjArrz4694DIiavxUkxmZUUFf4o/OsNMnJtdn5WsX1KfOBcl2Wp1HZCmMs2uGp49h+VgUvj1/ze63UNnihqx3pwmo2vrJh4HRmNRn6pZyPUaSCGShljGPXY5oylpTAocdhWfDk8gKH4xF39zvc399jfzjg7v4O67risGq5as5yozzQ6U0ki985SHJQg/4SS5O4NYnHlTIQegljbM7OMA4jrq6uMW222Jxtsd2eox8G9MMIIm13zwI+jKbvtYrk/HyLzWaDi4sLnJ1tMY0CCBxRidPGGFU0Bohx0fCNykubDkhmHA47LPOCmzc3OB6PuNvdgbXPwuZsI6EG7UHivNeGRx2GaULf93hydYVxmnD95Ak2Z2c4u7goG0mO8n2ZpZQz9H0R1YmraGus81GYlnha7snMiJyE0o1USrtq+3TdO1NWcCk9WYa+wzT2SNEDcQHiikwOrMbek1X8VHrcaXjFqfiWVSeIaqYaGM8iUscZ49Dj2dOn+OD993F9fY3dYYc1rtjv95IbczwWoHh2sYVzklDJAE5iJMWS2j5km3SNxfZwQo1mAKUpGRXPpwAl8xRzLoaSUobLtfQwLRHEwEoeycv9dE4enBnsHZJfZd0MmgDotaEiVwNWjB1YK4nqnqdb0YnXasCv/CObp5uF0VIjLs3PMgDtpqSJpiaCJUYcaij1S6OUMS4EZP3ddQHktaEjNeX0TkqVTbS87CNsYE1fq8J15Xx1rnA5R64lY9yAiaQAQBM5eU3SIl5bRFNMcFFyKbyFXLIAEGadX05CniZXzioYlWxwGSU0YuWsqancSuuKeDw2LeXruNcwiZ23YQvzXsV54ZZ1rs/qx1TQU+atrtcWYLdMhIUB29cwswjJNZ/7+DgJNwBNxUUASDPsSItztZ9VCf0Rt3e5fIclBP/uhwy0fK92xiaFBQZ4lAUvlZNOkpPLPCeUsvVyzbKVI58OrwIPy3Qy4FEZHaAJ+5VwjjFPzdIiFD/9JDwCgFQ92hJFy/5D9RQ+D3hY0qnleJwwHvpaBlp+4MceX2HwIS2yDQFTVjrMktHbSQ/IlevrAV2YDa1mXqVsgJphD0ZHhHNtTzz0HZZxwsW4wW6zweFwwO004rgsuNvvEGPEvCzSspyr15lNsMw5kCdRsvNycz2J5xJIqiZyZvjQw4eAs+0WwzTh/OIcm80W2/NzbLZn6PsB47jRvA8qzAcpRB3HQcHHGcZxxNnZGTbTVJgUKe8S8OG9nhMBKchndZ1toKlMWu8Ya98BOWNZRgyTCYJ5jJtRkiqbBknW0KzrB4QuYLOZMAwDui4g54Td7qHWuZ8s/Cx7bRIDmEyHhSXHIHQdTPDKmmIJWNeOmM5ocPFiSY0yKXvhlAannIt4ks+iy+LNO4dOfCKRXCZICacaIE65VgUY66QGjSOB1oiBHKjr4KcNLjdbXG3PEJCxrAGdbexEYpTXiHycIfL2GojL1Vsruw+pd2ieG6FEHQOrGFFmGKV7crCEKU7KGVlCLMhZaH79EkpJEprXCM7aX8QBFKQ01RNJD5lOqo6C9qOISftYzLOExWIFsdD14JUKL8Cj+a8ZdplzueRiSFWXJvcaQGk+l3Mq4KOWq7beI4l3zwxaV9FMIILPGeQd2MtritHyVDcOreSVKVV38AIQ7YXmoRsro0a9MAYKpCTvRKtJoraIXyTRlNdVJMGjSInnAlCs+iXXeQMghwCK0h2W0ZwzozA5tcIjw1RoSdkPSjp/lJ2Q66MCPISJaw2zAlquFSP1Dtqzjb9XegrJM+RdAXACfB8bP1JTaUN6CgJOgHfzPvt7OR/dB53zkqNV3l0//2TPIdJ9wr43o9AYynBlLVeyz2LbY2whQhximRZOwyauAUbycqeTyUpaGQzrrm1gnW1wCcUmlSoVAwXOoXQFN3ahYULs9Fr1WNPmgP6k5t8n96FliGw/peZSyf5mzJJ+VvlbC1DoZH78HgEfHr7zBZ3DxLNynbRlbuiG5bT+GoDSgCiDV6lbWZguS6dCAnAxjkhgXECo/TVl7Pd7HOcZbzcTDvMBr2+CsCIPjHkFFpV9BlgqUgBkJKBzcHAIrtMmZ5rQ5kiqKBjw/Qjf9Tg7kzyNy0vp8bE9u1DwMWKaNpoU5EpoxCbGNE0KPs4xDAM2m40AjxDqYvDmmVhbeUbOTjcpFKORdTyDF4G2QB5rXLFdJvRdj74bMIw9fDABHxlP7730I1B54HEYEfoOofOIacVhPmqDOI++60p+QM6NMVEK2uKm5EQOfl0XSbRVdUQB6tr3QJmmhvsHsSTYOpMmVnq7BSEuG/iQ1/TqzWTyqjqLYmAMfJAaSJ8SoCEo67g5Oo8uEEYKuNpscbXZwueIxS/oMmsGfCpVROlwBGuTMJuypSOueh4nlRT605ySUHazFnbQ6W8sxiQlYQwyZ1WolPwCB0g82MTxaAVnXTOBpBEhtDKk85rnI500h77Hsq5YUxQDyhK2kTlWxaKcKT6Wq7SfNUcB9rOEK7T6wmSv7faezBWtFkkqOKUJPtYxNKnnnopBgbAfwTdJvhp65QpgzQ7I0CoLJjfgJOGWGTKO5Q+oOhIKWJFFFyNbV9VVEj7TsoLXFXlZkBeRBE/LUr5PKqtS+d3uaE6plkU0R81F0CTRwkKhgkCIVgaYTs7ZHtxaiuZ3kS2gwjA/BrkVeAjgqxPQgYI/ZVUeH2qoZL23/6vP06NH8bSpnetmoGVvK3i1+V6dDihW3j4HLHlOOk6WY2IhLzPWFTjpe0hHVZ0pc8IM0BcdOvaan0gVVNmadrWKjZErroU5HSifKYxxk3xqrJciQBNjPUENra2zl7ZUpOpRfWZcm+GtIKXeD6u8bP9+OsanK/2LHl9Z8OF7UYgsJYfJq4ckk9tKvywxCixqlOrEqbfs4EIH9h7ZmchORKZc9B0cgK2X5M4EWcSry9hsJ8Sxx9U0Yo0RL58+weF4xO39PfbHIw7zjIfDHktccZilS+0ck5TRJgEkkhxXpbYdecB5TNOEfhxxcaEJmefnmCbJ35imEV0vBt85D3ZWoSOiV877Aj7Ozs7QdR36vj8RZQNkEnlvpqsyCIC22c6SzEcq7xwsAWkzIvOAbRbwIW3LpWKjnfBdCOhDBwriAXRezi00yN13HZzz8CGocRTPNimaF4bIkqlIFz9jjVaJI2L2nQtCnyroYM6IWnJM7CESzrKCHDm4nES62EuwbQwB2TtEmIR6xGyVB1lFn3IS45AS4jJrwt6KFFfkdcEyz4hrxDKvWrIoc87BId09YCGP5eEe67IiHQ/SW0jlojkn5KAy8l2nM5xr6MXJ5lPir5YB39CsidukNTWerr7Hlj1rfkPW5NK8rDCmT5goBw+RmnYZIBIQHhgIcOhIuuR6FhEzT1V7wTkHn6s0vj30pOr52dnU/zR7JKH0UFJvrhpIYxMbYIkyRMVAkOZSIKkT4qTdAQOgRChZqikjBzGKcKQKuFJemDkDylLKOdlXajURE8g1JY0GMGycjaWoSn2axJmRlgUpJazHo5S2zivyKvMoLQIG4xor+ND7VprJ2bgq8CA1snoaALgAd8kPyZrvU8fMOY9elUaNcSqMCYtkuxn+RnFc5AvIlRyGrJGnGqJoQADVfAfpjFss8CPwUQ2/XAOfPE4+27xpV0tgWddBAUNWmaGvNU0b9U1RLsr2Gf1bNoDv9Lz1XhvgszwLM7DFeCsw81YZYmNAMtrM9dyLES/Cf3YwONf+LVCWJqOGAwV86HeQAzjWfBgApepCH9R850mohCuAblkKesTYWGi7fF5hQfQiXPOZ5Rz1b6752a7zL3F8ZcEHOV+YjFKv3ExsyfA3CF8zvW0TclDP0jtYHI2zVAgkoMRQZe9yZRInB3gwctch+4CpH5CYMU0DDvOMse+xOxywPx7hHXCYZxCknDVnExmDhGFYvodYkg3hLWcjYBwHjOMgeRrDiGHoS9iksxCHF7AiPW2yUOBBylxDFzD0gySAhvCZJOPqRdvkbRPqIMibncZEc0nIdX1X/NT/b3tvH6tZVd2Pf/be53meuQPOjHRg7lwVHCxKBKStrZOprW3ChJcQg7V/qJ0/0DZSEdNSLa00AtrUYGlCGg2haf8o9FtLq0nV1LQmFARDHceCUKu0UyFT0ZY7pPgb7sx9eZ5z9l6/P9bL3ufce+cFmTsXOIsM997nnOecvdfee+3PWnu9DAR8VFLOvQQ2Q8nX4aT2egALyaAT0js7X8xnvvr+rG46CYsNUpVUz5G16BmBkChYZAcEaJBYAjg6l/+2uhcWtsiConKeayIQgZoGsa75DJ74fN0lNn03kzFS02CytChVNmsrPtiMx1xdc2HM1puoizigWVjCxFeojyygaWo0S2M0sUZTj83RjyqP5D1c1cCJWmkmTa8gQwCUCSBABbcXJ9t8v26c4p0uxEBMHImlv6TaEsSKROxfoMcF5vRIUjuCYEcH5aZoEQiF34MuuGzsKISQgMkMUOR/VPxJWbaxFSIVG2LhU6LHHRB/ENKIMn0PrJMUHcg34Gh8Djq3PATkTViSbRClgyLZdUsgSDCAQSJ/SMYVjRY6IwlVTYiTBik2DHSbiDRh8BFrrsJKMfFPtRLoO1MR8QLwvBEt2jYRFXAyduXPkq+8DEtvB5UF4pehiINy752AQe/Jxpd/z0PacpxsWeNIfKcpb35EtvnbEUA2UeR/We3O4KPcBIvNT7deQDOFIjs7guzVSflQcEDnCFsc+Sh/eXRM0Rb5acdy6leSZ4r8LJ/hinuVDQqEvN6NEi5Rfop9V5Mj6ty3+Z0Hot1m9QHxgKV8VeBgWXGdHeuU4bKumCHLxqL1GdrkdFzbc+h4ad2CjxCCnbGBAAqqaURblBTJkm45VwgqADr1uIYFOyKCCNF7tiJ4Nn9SSuYQpPU16qgpgAFUFeA9Nm2cQhMjzty8GUuTCcaTGgf/v2exsLSEI4vzGE8mOLwwj6V6gnE9xqSeyBFOhKPEzm0+wbmEjaMR+3hIlMvU1AYMRyOJDAlWUM2HABcqM7NunNqIwXCEDSMGHaPhULIBaplrskk18F5SIThxEJTzdUgGwsiZ91R+aK6IYcU+I5whtpJ//Ew17XLyNYdBpf4kTjRFdgANoYKrBpikBjFxvRZKZJsXoOAIQJMQATQNt7NpGkwmE9R1jVosHwS2hDhHHDUSvGXTcdTAwyOgAlGDhoDJ/AJckzDlAihF1AsLmBxZwHhuHgtzc2gmE0QBGs3SgmQiJAYfdYOFhXn2QahzGG4zkTwoRxaRGoJr2OoRXMCh+QkWR0PMzwv4aGrUTY3xZIzRUMZ1wNrrIuU5qotdMx+W4X4mCPQYI1SS5jnzTrX24LMwIQE2Ufmm/Pae5/JAjgR8ADWE5D3SpAFVDVJVYzIew1UV/OISfFVhdETCqwcDDsukhPF4iY9JWqGHIszKownpadlPw8OkfilspYmxQSI+osrhqcROz02Tw1g1RFTmk3OVHOnxg3V7YCOZRHJwDnCRxcRREk2DVBNSyI53BHZAtkJ9BpqTzV8rxiiyI9WNHA/liJZY8xHSZMyWtDSpJZ9Hw3k3tLYHISe3Atgh2uYHC3TxbYdGjeS8EeyEKgUmLKIHomT4BKDWDSjPJ48MPAyAVFrAE4Dj9WW+Q5ITKEjUXntsZWMnCWCOVliKR4PI5rIBhdLiIUBWK8jqfCnsS51JJGQO2uqYqQCD/cIYYrlCU5fLakXxPrfSecCJf1jxGgIAlw+FiLBCPA+gVhF9hXifcUIwubcRsO5YI+Z1KuA6yREQ72NFZnOAU8oLMT8ZNGUw5tgxWW6wSJPgbbyzCJF5YPk99J6CwSqTXcm7gn9oX1PAYQ61wMnL8xFjxMc//nH89V//NWZnZzEzM4P3vve9+NjHPlYsVMItt9yCv/iLv8ChQ4fw1re+FXfeeSfOO++8E3lVPqMFMvIiqcCI7FAHJHV3luRiKjx0sfJzvHghqwbvvTdEWXobO8eqYa5BkK/Be4yqSjRuj82nnYbRYIBB5TGuJ6hCwOJkEUuTgIUlLj29NKkBx+foznsxhwYMQuAaFPJ7JUcWwXHm1uCdOVkSEef2DwFV4NS9QbPtyeQpWlrMG4kYKaMgoBpHToREmmK87Grxz5Y2le8QrUvkG8vMdsihUy26aJeNKUikTNb+9Oxb/TSCAqqU4JJEDEAz8slzkuM5QGBHvhixBI+0NMYgspPiZHEB83PPYX5uDktHjrBZvK6Rmhr14gKCY3NznIwRmwbjI/PsCNs0EtLuOOxYz++bBN9w7saEiImbBy2NMVlc4hoq4LbSeALWvR37LDjYhg3oMVgGH8u0EvkMzoEqDiVNLgt0tqRBNlDIxsQiT7Yh1qvESkJFeKgPCRTZ1Jp8ANUNUtWwg2MV4JoarqrQNI2A4coiU7juSt4gXQv8FMdDxYSUfY3/lO6yQ7GEwVp+DAHKkpeCGrEuRIma0uONlCRTb8PHLjJLWWBHCUUNvAtLanIOgZQjGpEjIElhrpuFJnki3UwAzUWTtBhc4YAcJ5qQS0ETGTBLdSNJuyKHbqe8HtTqou7Y3ju2HHgyU7taw+w/J/4G0jJyHsnlTVMTjEJAXUSEVVh2zjZKkAJctajIUyUsPFQVErHzdoME51POL1MAAqcbP6mMyFYuTk2Q17tFHyJbzDgTq2cLr8lzlQkFFCg3QhSgsBRYJl+Q14fMzXKtFTuuCa5SW+8CZ7VP2F8m7EoqZrztg+UlgSG6tr3PIfCpG3NTbPydBul4aR/L61T+bXIFGYAUcsX8Okqm2XdLwLFCWzKTczuM34BrcfDodELg44//+I9x55134u6778YFF1yAhx9+GO973/uwefNm/NZv/RYA4LbbbsOnP/1p3H333dixYwduuukmXHbZZXj88cexYcOG434XOQ/4kLsjFRs9PJJLshiTlANMgAsYTUmp9aZhU2STxNPZFGVY+krv4MGCJ0kCqeEgCEsc6sgJtiZNRGoaxFhDi5FVid3Ip195BgjAYr2ISV1jfmEJ80sLWBwv4rnDc1gaj3Fkfp6FrAP8YAA/GOD0aoBRCNgYKoxCwFQIGHiPgXMYeYfKO2wI7C9SVcHC8EeVRxWcHbclO+stQIErPKJ1QwCnjE8i2FPNzpN1XVs1U0iRMV4rXJoaRU4L3bQU6MTGITrH55gqWEStdRKaqvlVRtWAWQ/2pE+xQaOCuclWGZcSQhMRKGEIIHonbhkRnhJCihjECiFW5vyaNFNkTRgvLGC8uIR5GepRFbgC6sIiFhcXsLSwgFRP2IkUHGExXlzEIHgMQ2B/oKbBkbnDbPmIDfu2DIqaCks1XEwIMUiOBWBxfhGL4FpDzjtsnBrBRzYBjyJh2CTUE3bOLGu66FFC8ipUBWhozhg4y9IZq4qHwOWjBi3gwd70cn7rAC7ECNHkeSPm0PUKVTWAH1SSf0TrdDoTjIw4PTAawIVgNWlC4cytYash5DoWTs6SR5VaIlgrdWKzdiALr4bOIk2ORVxsj8QConPUapBEBqokmTepjmaJ0HBFJ/Humh04hMBAxGVnU802Gzr1iOAcIgID8iICLJGGsCZxdK05uCVxFFGKCc0kp+C3/sr3UqNRLw1HiPnA4+c0xbeDD7r5e7HuJpCp/jnk0pGUYXcaIEx8dAx2rmfQI7mAmojkHCgkeKdh1AJsLb9DMC3c/HmkUGQ1GJpDq49ccgJNYxZYnn4CDghg91aWVWx5ydaC0mzhwODOCcBiwwXBO07Al/OVqHJkgky0d6lubJbqttxT37pS9ulsMxlZWFn0Pv28BURMyUW2+KrRyWm/8zs06so234zCJDyXJEW+KKESulvWvnGyP/FcFuBcOEADaCX2y8AgWz8YaOUkYwZANCVECT4MMXTAR9fXo/u+0vphzxMLV2GtORadEPj4+te/jquuugpXXnklAOC1r30t7rnnHnzzm98EwBPyT//0T/Gxj30MV111FQDgr/7qr7Bt2zZ88YtfxLvf/e7jfleoBlyKGJCNj7UkEgsEwIwMzlmImxNN2gNy7s8TrTSlsoOTMBgMckjNd0HNjx5e64DIWW8iyb4IdlgLgIU2uWqIoQsYuICpQYXxhhGmqgEmkwnmN2zkyAdKkCIT2DAcca4RBwyJUDURwTcskOoJT8pxAGLFR6ISBVIJMAnBlXliVDyxFUcWYBgMrLosEadYBhwoOTbda/bE4Hmhq2bl2eoSghR3AsziwVYQPdZxqJwmViu0kkRSLClCzz1lfbQcPH3NpnQnvheApPWu2SGUUkRqJnmTYfSCJJuyOc41GXzU4zGacW3CpXaOk6pJ8bpYT8QHgh3NUozAeMzOqN5LRAWhIk5jRQgIyXHGR57gGHgWtMF5sw8rf5LMSa5bw/0eVBWPqwsS8SRT2oneSMSlueGsdgmnZ0ZRywEixJCdflW4oLDcUdY8leVWciqqr0Qt/JQjM5nPJkiDB7z4eHiHOJ4gOYcojmWk4+mA5AO8d+zLovMgSG0haajXikuOJINrbqtFPRChiTU7yiL7mejYUyObq0S5JMn3wb5fOm9t/wAcIXnKViPlVx0lS2goBKbMW9L8E5HN2wA01NcKWEYGP1qaQH0+nCglvMcx+HCU/WmQ8hj5JCnSZPw0MaFz3vKW2ZGZhc56JMn7oUdXuuadpPImz3V1HBGiZyWtBB4KNrPmqj+d8Ej/KWDj60547INaZCS3TFbmO2CjgBoChPlanpEAqQhny6aTn56Bl/eEJBFUSQCMWS7IFZahvPGrXFK+6PwqjBrcnsKi4q3v4ojrcg6S7N/GVbqFG609u+im/E6tq/r+4LXYhVrWMl+cJj/MTipQXxAWBV0EkEEU/yj+tmcqyMjH8Ao+rM32LlUmnLI3AxOnvS66Zf5oJbh7fnRC4OPnf/7n8ed//uf4r//6L7z+9a/Hv/3bv+Ghhx7C7bffDgA4cOAAZmdnsXv3bvvO5s2bsXPnTuzdu3dF8DEejzEej+3vubk5bthg0ELgbP6EpHEWE5b34jgnZlQp0eqdB7nI2pR6pZspTxgc2ILCmIQFdBTkH5z4PMSIgASXHNc4gFSclH+1VDIchQFQDYHRFJrEacGXTh+jbhosjpfEgsKpvRsiYMMU3GCAIRwGiTCoawSw/gAQUuP5nLAacH6KwZCPaDwwCK5IapYdfbTYEAOKgMFwhEEVMBoGEDWgxJoaOQYgPJsCT1AEEw6VJJQaVLluhvLPwxtgGQQ+PrJ6K3KfJohiB0kVUllQcxpqth74mNhHQCwAsalB4yWkeswOeuNx9gEgQiOaAvuFjMWkLZpxQ2b2NuERU/69PKYjsQwkgmsaJACNzyF0G5xozQgsLBrxbSBgoIW9wGZ8bwKp0AYAAAGj0dDGpxLHx8bVLTNrFlwu/12COpmv6rxPCgAZnbQ0GVU0qXimll63DbOJIEgxvcCbng8D26BILDMkeTP0+EYGuBB6DrEAHToPGi/Ay7OW5qEgVkIFlQoTewIXP4yU2sJctM4kxdBIs3PaegYgc5A8M8l5tghorg/ji2yuydrPloc8V3R+UNnNor3c1hhzOKxupXLWAU2aps/I1zzn3EhqsXUWJpqjFBikcV5Z9mkpE+453bDkb6uw6j281KdBkirdBBmTyqwZXpNLQRUwm2yWJZRKHrkAIMGjAhz7rJGnXABPQv/V4klEcuSooKBt3nc6j2wiQyoZAwgw3mvoP2lxxSgbNiQtQLLZyJGEZhHI84aKI2QDRGqtME2II7kUBEZihabRBHpEbB33ubQ91zwq9mWZBW3H/jw3FDBVlRf9OQmoTJIygBU9Et7q4TkDcqlWW8jW8qchP9j0sbnBP1GkTld5oQd4+qXMywRx4C3P2eU2QiFnfJZLpdzPa7aUbkenEwIfH/3oRzE3N4fzzz8fIQTEGPHJT34Se/bsAQDMzs4CALZt29b63rZt2+xal2699VZ84hOfWPY5FYuhtHSoxzeJNYMdiNm/gJyczcoz2GlHtCivoZVF3QIvi8YBAMnCAzPeOzgEVMMhm+lDMJCjgsmJcLRkRI59B5JnfS8OEkaDgVTKTYhgR8DFyMKxSoCPCW484bDApoFrBnCVgIFBgpMwVecd4qID6hquGlhCLi7dXnFKckkpHkKFkfeonENFhCEcXAgMgFJCklDARo5frJAYAKoAEj8T8omFpGp1Ys1A8KBIaJqEyhe1CUSoTCbsbKlChvR8XIrXcaKlWoBIziqqPh+xnvB9NYeJ+mIhRMkH0TRcpwYNa2YVheI8HLYJ86LPgMQ0GjCYdaRRAe1aJapzKjBVsukBk1sK/0ywtc+kRTjLB+pz1NKROsADnZ/8joTiLpnzsimpU7wKCum7A0kmWFgtkFIo6n8BEA1XkYvjdN8QX3yCCUbT8B1YszcwzppaE3nclT9ekjt5AWt5YArzPHIan7K8vWrYFgGlaesNuICtOJ4yEPPBQKDe1kpiJTzVow2eE3nD4qUvyfEKYa4DWBHMMsJNEWaobpO7lzcKm2PSBnkWyzay+VO6TDH59jtgrDeZ4+Hg9OiGXHYChoN3lYVXc/VSl+eozlcHDuX1chQjDq1Ft6AacWtfETlcHrlm0NHidl4oMifVmqF+L9JJsWSJM6oeJzsGbySWZ/K5IWq5cMbb3JbW0Yo1Qw+s2uCBE/RR2WL7nvq0tZ6zyv6qwMH6bJ8ypSKyxVyjrU3K8PYL231KBRiA7VNQ8CF7kF0DQcOUcytIcn2Q3W9XvB7LGcOKn+17Xwg6IfDxuc99Dp/97GfxN3/zN7jgggvw2GOP4frrr8fMzAyuvvrq59WAG2+8ER/+8Ift77m5ObzmNa8xbaUrjJ04iirgYB5T5q4j81wmyKJJ4grliR2nEoG9u2QByYRXp50kg+4C120BcYpjkJ5TywJxMS803RNUaFUcujuoJFoFKswBt7iISYzwJAJcwvVINAfEJP4uABrRUiWXQWwaoIosVGICBhXCkKutVs5hKGfwAzh7fhBeNCSRDnVjIafqROdkRhM5ccoTNB5SBnfSP4oeMSTAy9FKIZyRgPHiAo4cOWJSOInFo5lwroxmzA6fFBvx+mfwwdkY9TydE3sBWsOIwUEiBh9Rog1cIsAFBD9AcMEABG/4zrR3PVctBahuSXzkoUduzsABObZWWAoFJ4JHnJod0BFMWaDrZ/keZ+3K1hGYMLRrxecty0fxuYpPE4xqCZQNLxXfV6GsAKiVdlmued08SLQfWU9W0l6Yxg7YeUMFCs1c186kzr4Y5fsBO/pUhra0Ocm1k73w2xt8y+JhTHLmREuSCl3fu+J3C0pRN/vcBrVeVV58nkKwza09Pq5ovyt+6jsLUCO8sv1A+2bvVcGR8vxUgFz0ozv6dkzquN5L8MSKQjFTGPh5AyBO5YhukzI5LRze5XnSAiDl/O+0qdzsVz/vd7D03Ma3zlja2MpRJKn2L0+QAjflRmp6fDHPrE0rcK49esWYgzLQgG7uhZWg/B51npE/at1YtlNBdLF6+D1u+ZzI/M8tzgCvAB7lLc61fkdxjzW5QFHWNofWeFuEgTWpM++Xzccfj04IfNxwww346Ec/ascnF110Eb7//e/j1ltvxdVXX43p6WkAwMGDB7F9+3b73sGDB/FTP/VTKz5zNBphNBot+1yd7xRstMxPHQ3WggXSgK9JSJ56x1MS73mibIpXD3ZKaGpNvJXj09WDv6oqnoQKONSTPUWkVMHFyCm8KEfgJOsDZ40U7G5yd8tpr+DfnbcNIammJp79wARpElE3hFgFxMEAo41TqAZDwPOxAA1HnOJ7Q8KQHIbJYYSAEAi+Jmg+iTRZQKyX0CwuckXT+QV2Mo2NbPwFkHM1yHk0foyJABOYI1j2+jcAFuU83qJnyI5GnCwKywLZ1BatwLklOL29+plQlKqbsRFHKzZT83m1rAuCaPzsbcnH2JwwzXvuhFaKzE6wTJYFEnlB6b3dTdT6W/y0TYEAoHiWCi3pr4XZORV68gsR+yhlPQdWrgVFBGGpJaumWm5IqrGuIAxM4JkmGex72dmw3DztC9CNtKXFyqaeNwwZS/mO9jkzWcFA53iEyI7PcrRDfo6rBlBHpu5YtNvY7XAbyJSyQdsQy+MaHdtGFQkZQ+MRO1qX86I8WmwBRNfRtL3tn8wDdeKwkVGgQbLdZV6VoNc20867dAy9Jn5Dbp++wqIeSCB14Uyai3VK2kGX5xo6/eyCNVUwqP1hoSQ7PnYCbIu143Hlq/YDZbRiJs7iLDJGUiAk8c/yNh+zg3YIwdaCzbGj0Gp9a9/D/n6pkB02Ps6xpar8uuvypAT2GZykAngAWTbk1P7tzV4BEOAKaVF+l7+oY+/12MUX80iBhJohkX86+57PPlHeLZtzq/HwhaATAh8LCwvL0G0IwYT6jh07MD09jfvuu8/AxtzcHPbt24drr732hBrWFT4tU5poaXYNEPM5TDCSaIZJJw1kcTjPm6lzHDVD7JQIp9nxCgFQLkiCHdmwQM3FzdjbnaM1SNsmC5NscmoKbyBwZi5EmVjZKiLCyIHTe8MhTSagJoAaSY89aADHgoQmDWgwgGsahJjghg183fBZvMAdIsJ4soC6XsLS0iLXp1laYvAUi/NJ2SQSshkzRSlolyQiQc67YxOttoTW99BS0lRIczWL2tm3HL+kxIXOHMC+Fd6xlpmUj7I52dg6TgikkaSJP5NRlt91JrTnzkrAYqXPVwIf3c2FSlXWfs/yr6WB6VpX7cm0EolwKeatihgTWS5fU2dUjWpaCXwctZ4CFRoP8ibQvkXRU3tNWedaoCvPFwUOpVTVTdOuQTW/Eoy0FQj93JwBtc1F2ws10l5kbSyAoYEotI/aFESafEgdywdEkKe24+HzEcDdzU03oFVuLsAd2mPQmcPd322O6TzlP4rNrz2v+Uxf82BQlk2d/qy6OXcBb2d8WEwSSsyVV2aeswomdN8oLSc8fFLHigyiyXtlvRf9ZlCgXM586PbgmGNW9LEEAst54lqAg8pOtl/Y/q4IsxZfMnJrPUL7m8GHSbbWmNtPEQk69np/rjTbaaJ+R+YHiu+W7V6NZysqPV0l4DjohMDH29/+dnzyk5/E2WefjQsuuACPPvoobr/9dvz6r/+6Ner666/HH/3RH+G8886zUNuZmRm84x3vOJFXcTKpKhiiNZPVCiZb3aS8nuNKnD85By/auvPiaFiUllaN3oM3PO9ztU7LZReyVqHIX035w6kpOCdp3TX+Xxz1lNQqolaBlAjBczgiUdaQScCPCoRIQKojJ8QijtiZp7Ygc4EdygaDAScpqziU0kk9GCJ2AlsaL2JSj5GSpHSW2hYMdhmo6DUrIECJNSxxPAMA340IkFA5Fgqi2XhguGGEkfAGAJw4OzYQx8eaOLMoEZ/RewcXSBIqOX4IwRIF6Xk5JCI4JbCjMfj9DBJzyKACRt08UkrLtFj9WVWVzd3VzPXm9FxsoCk501iUyu/7zgK1v0LrB7fxaJqYPbuQC64tTcxyT2whVK1N56pmh+0CLLPoQDZj5IR7tklIKxJIrHPUAiFd4FFJYcOcDZXsmJOzr6pFDm1NOQQL83XOsYOjU6DDL4rFezIIVB5k0znK78n7EyV2tjVeZ2fZYwnM7ubfvaY/da50NVgQLZtfcMtBo7XMeXMgb1k2ALN26PgAYjEgWNKufJwgbedkNcxbTUAl78teCCvTMt7IpiUPz8DUIUd/oW3VUPnsNOeIPDeG5bxjCzL7siQ481VK1ifKSo1F7+hb8rx3K7Vd7yzGfEULz9Eod3lVMrCg67TM5KhgvQMiWpu+WSjZKdoZeOlYmoDsVFpYN5gT5RFVoYCY4Pf2Ux1NX0irxvHQCYGPz3zmM7jpppvwwQ9+EM888wxmZmbwm7/5m7j55pvtnt/7vd/D/Pw8rrnmGhw6dAi/8Au/gK985SsnlOMD0InqRSPSpMs2rUz5dAXMbRv9YAMmUJudzNR2rzs+SLRBrpBqpkKgJWTND4Ab1xoo51zWUMmCG430mIdT6pAI3WQI2zmwqVcmHTnJbwH2hYjEm22isocOoEaOTPj50QcgcJ4S2zgpIdVjUDMB5AyVwQ+H7GoNgihaYIqKsjRPB0sUpwBD5T1lIcRalZyPB2DgA0al8yaJ34TkxWBrCr9DLReKwnMyJG+bjNcsguo0Q4ZBAflekNTuZZZQfXdL+9PNLYTW38vnXxaIZUls09JdWCaxW9pMoaGtRtQV1KsINd1Inb5A5zXKr5GEDHKBPX22qETQM38v+SW4jyqAc+MLwxWPp8lKPkzy7JgjG5yEfZbtLhefKA6W/CpvW8VL80apC8y6mXeR4jvKEEB9tpg/uiaK9W9WmTZq1/PzLvAoLR1dqwmz41i+DdmhuEXdjUaarl1o3daRKy3wQUA5pVQu8e/GZPnM2eZDuqShU8fZdxy6Vqq8cS/bmOX/emjU6pOxPwPCZZQnWskWG3/v2Brsiuv6qNLyUT4+Wxm1z+11fzTrVck/GyO+uLzpMtetn6t0sWVBMMAgnCP+m7rfdq2X8/e9ywqdPYtabXTMtCwOnMvh+MhzrAVwClmowNQVnx+NTsSqcTzk6IV+4o9Jc3Nz2Lx5M771/+7AaRunAJCcixdm765g0BWmmo3l/UitvxUEcO4A9d1IlkxIn2MOmOV7NMROnlHOn5agUmig2qVcT5IwiWJCXOIQYK/3eU58FKRwnCZ/Soks2VmTymVZgCGdVyaYinNbud40nCjIBRaOw+EGTiZUeUPlTawlFFOFjpo4ddFz5IK83QSRl4RYVTXkyRwAX1WWGp+IEGtOm91Mxoh1RFNHIHF+Dw1jqzyH9oYq2FjxG3QdczuTJUvLS9hXAcPRUCJ/vAlrtVg0Un0VgFlGBqKhr25ezX+XVo8slFPeDFeg49UiuhrYSsuxK6RNsy/HWcYsEThkFRmGO++5/k/F9ZL0e+oLoaHsqtVm4VTu3SR7ebEGYReLfiNv8hJdZtWL5TjNdkMULNTjN018BZcFta7LzrvgYCjUSZr0ICCLxIeIUkKj8yUmc5p1Lpiy0dLSBZh65Vnh/wHwMbNWJl5p7uicSym1fE2Cc6gURCgoKPZtG8e8q9s7yg3VAJ0AQ49ONJD4XLG/RxFqK4nlfBVa80Z9EVrzzuU5ZflWCuCt7WjBAOr0v9hieZOT5FqhDdzKviUiTugomYotjb28Uy2EJPypBgNbHNSV14VVr8vHoskt8GGlajrXdVw451F7zDqdyYBDNnZ7BhEiZTmicr5lvdH2aaIx5Gva84wjMsCxbrlyTrVBlQITU85C2wJcjkWXTuRIZWFpjD2fuhPPPfccNm3adNR7121tF2jYj0Fx/UGih5ezIDvYZC1MJkAiiXJRECEhvHCAi3DJA1GQLbGA9ZKKmv9FWaUyYASJfAHXo0gkA66abmeCF06L6tEeBNV64snlipTpmoYdjqNNKgDJezSRk6HpwlctVxcatO3iqOsN1Xp2WCVvE24goXVeLSQJgC72BNNoq1ChChVg4Ew1S4kUEmzu4BCSapNAmkxQTwowFkUYxAY+EQYAvK/gHDDwlTicioMfNCSt8PnQoxI4EFXMh2Ijdp6zwXZNh12tFcjgo3sEsZKmq593ib+nMUTWjCxsjzW39dmAOTKXG0L5TPtdZYkrvNOL55hQ4wmaBYbLG2qOfGjzqHyOvkM/yMoeq1SasVSTpHUPlK3atPUtgyZ77DKWkoEMtoTBQv7ESM9HqPyw/DpXAKzSsuM4C3ASZ7oAtQLKZq0M1MRXq2jILQ2xM1dWE9hl+YZWNlCnrGprrCX7dOPrRiS1xqkLVsv7ZDMqrVu8WssIEptI1v3ymeXQkIxf6/0F71GEyhKoc09eEQpakBKf1Po2P8tvlP1eBry6slU/Ux6Wz2t9L9+/Ek/LZ+uRh1wo+ECw4m3lNegY5Peq5cJ+p6QCO/ev+Od0LPT7Jsdcu3+ahbpcAJY4LI8rX3GWMEz3pTLDaasN+j3nOhzOPOtaBV8IWr/gQ6noeFtMionY6QKhNnIEsbne7MZkk8ycUB1YOxINqGR8ipHTFqvJv4yrl/c0KYJcuQBW3vhKrQEgyVDqc1VFCe2rnEel4aLspYkBPGKKiE5SYhQJjrT+DIMGEo2Iz5yrIJk4vUOCF09tnmBBExyBEImPQUJkSxE7gnLGyJH4k0CAj+XLKGJPdYmYX0FiUNY0NYMI1ZJklToAlWio3nsMJR23pu9OMebJLm/QTZMjh/TduTASeeLqn9KY1QADkMFHNp2zkCwtHDpmK0/HUjDqSOQnlbRcXHaeRbyplrtxC3B0HuSwHHwYNpc5xrO5rdEZeOscSZVHBK2NR7W9Ygyk0/ZeZw8v+iv3d5Ro+Wo+4uDfO52j9p/lkZ7xcQVwkJCdFpcBBSdrW9Jbu8Dv9XDQirjEE2YZmFhNC1zNhA/HZ/PqY9P1/eDVlsfQiQwqH2dFAF27r13gsRrpEQTXrXJtLb4E10CJDVrgtQs+zNKB5WvCFeNUPt8ZMC0AlR5fJk6e1R3LshfKs2Ugr/P+lmUDy+dG996Vxi4DC+XfCgpMq2XFd+WfTt8SL2RwAe6vKm8F85zl1ciLKh+HFAJNn62WkFJRce3HGpjVuaVtAFqRZK1+GmBZnbrrV3nz49C6BR82sIUgLD8vF5aRE41MZ0KWvxJBAZDnqJQYIzszeam1oeCANLudutqRIFYn80lC3RxbQjJocbYJJEh4XyI0kpI5aCKvRBgicLZQHUwU4XPeC9BJ4BwAOaTTO8fROpKiN4nGWGah1HoqVeGAST6AnEdU97Ik8fOOzCQO4kiepOicAKprtjJFPiJR0OHBdQq44JhkhfWVZd0cT8ZYoIQkWQMNrJCkrBcLj3cOQbWnmEw70qqlkIiYAM/ZFaucAAoqwDzx5lJkCMxgQoFpaZWSjYDymGbFrNQO9XpH04A6uIpnT5GxsTwCa63k4loOp4WAMrWcUeestiNOJeSwPHrR8TYHU52/JSAqhFsGwO3NWq+lYo1pTYmjCaSie3avOa/pBUf5CFNz9OixWiLTmElD6y3BVwGUOo3QTdaBI9bK/pT+OQxcKIcfqiwBOJwykTlRp5TsmZYQTEHz0UgyVJqVlgD1WYLntSruXbLJZYFfggy7VtxTCvxjkWrbHL2XSoOGAYhERch2GzUue15bYTo+AFS2VUsDRMmcplbabJkiA8PtEPg2KNY5qc7nJRhogVlkYKfXuvzTPoUQbG9ZxscSaOdv8nd9HhMSGULF7aRLrQNCDECZBQpwweW8GuXm31qXcoRoS57MmlE0WDuXHbSt7oY8V9rderasq9WA1VrQugMfOlmOLCza313k3TKP6+ZpD+AfcmLWFiIi8NgPINqZcBL/AxDsmERLZ5OABgMyIuD0yEIpawCSljpF8zewcvKJN5vkwBE2hXDzhWaqkpvPbyWnCOm5eTLhnSgX31I/FAYcnPHTpcDRPqECwVlIb6HicoROlMReKcqew4BqEBOqRiuNyvEMeL1UVYVAhAGI68AEB8cdw1LdYLGuoSG4apUBcT8VeHD67dQSGCTAMMYErVZcxQQfAqoYc40K0wIg4CNx0S7dSIj71j168U2U1MbtaIckgEfzldi4ujLzaSFc1TkYeW50FzJ1fofL0SOl8NMxaX3PZaAiaCDP8Y7FQXkHsLCO5b2lNcBLPZcVtLtUAE8dB0DXkIxPsVl23wvtzwprMYenZ9BRgpBUgo/OWfRqzp3WJuTNtASK/DoB56VFS/7pvDSLUbEBqp9ECKyt+9hOI98S4qFtTbLIqO7aF14GqdsjmfNsHEtghA6f85xGOQXhkGUcdyqDOXFzK8CcM5+PMqqoJUuLxxPIZEoXiLTfn/vdxDK9On+v0RpMxRoMA653pL40pU9VSlkGEJH5sUD5qjKMH2agyxfVlEuLTbd/Or4ACvnAnXHecUVnHUtiJTJK+6sqz5FlViTtty8sjMVcAYA6NrkdyWWfj85izpbZwkJkxypZkSnHSuOW9JjV1o1aPrR/9gdaoKccy5K64G0lKj9fGE+Oem9J687h9Ic//CFe85rXnOpm9NRTTz311FNPz4N+8IMf4NWvfvVR71l34COlhP379+ONb3wjfvCDHxzTY7anEydNYd/z9+RQz9+TSz1/Ty71/D259FLmLxHh8OHDmJmZOWpIOrAOj12893jVq14FANi0adNLbnDWE/X8PbnU8/fkUs/fk0s9f08uvVT5u3nz5uO67+jQpKeeeuqpp5566ukFph589NRTTz311FNPa0rrEnyMRiPccsstK1a77enHp56/J5d6/p5c6vl7cqnn78mlnr9M687htKeeeuqpp556emnTurR89NRTTz311FNPL13qwUdPPfXUU0899bSm1IOPnnrqqaeeeuppTakHHz311FNPPfXU05rSugQfd9xxB1772tdiw4YN2LlzJ775zW+e6ia96OjjH/94q3iYcw7nn3++XV9aWsJ1112Hn/iJn8Dpp5+OX/3VX8XBgwdPYYvXN33ta1/D29/+dszMzMA5hy9+8Yut60SEm2++Gdu3b8fU1BR2796N733ve617fvSjH2HPnj3YtGkTtmzZgt/4jd/AkSNH1rAX65eOxd/3vve9y+bz5Zdf3rqn5+/qdOutt+Lnfu7n8IpXvAJnnXUW3vGOd2D//v2te45HJjz11FO48sorsXHjRpx11lm44YYb0DTNWnZlXdLx8PeXf/mXl83hD3zgA617Xk78XXfg4+/+7u/w4Q9/GLfccgu+9a1v4eKLL8Zll12GZ5555lQ37UVHF1xwAZ5++mn799BDD9m13/md38E//MM/4POf/zwefPBB/O///i/e+c53nsLWrm+an5/HxRdfjDvuuGPF67fddhs+/elP48/+7M+wb98+nHbaabjsssuwtLRk9+zZswff/e53ce+99+LLX/4yvva1r+Gaa65Zqy6sazoWfwHg8ssvb83ne+65p3W95+/q9OCDD+K6667DN77xDdx7772o6xqXXnop5ufn7Z5jyYQYI6688kpMJhN8/etfx91334277roLN99886no0rqi4+EvALz//e9vzeHbbrvNrr3s+EvrjN7ylrfQddddZ3/HGGlmZoZuvfXWU9iqFx/dcsstdPHFF6947dChQzQYDOjzn/+8ffYf//EfBID27t27Ri188RIA+sIXvmB/p5Roenqa/uRP/sQ+O3ToEI1GI7rnnnuIiOjxxx8nAPSv//qvds8//dM/kXOO/ud//mfN2v5ioC5/iYiuvvpquuqqq1b9Ts/fE6NnnnmGANCDDz5IRMcnE/7xH/+RvPc0Oztr99x55520adMmGo/Ha9uBdU5d/hIR/dIv/RL99m//9qrfebnxd11ZPiaTCR555BHs3r3bPvPeY/fu3di7d+8pbNmLk773ve9hZmYG5557Lvbs2YOnnnoKAPDII4+grusWn88//3ycffbZPZ+fBx04cACzs7Mtfm7evBk7d+40fu7duxdbtmzBz/7sz9o9u3fvhvce+/btW/M2vxjpgQcewFlnnYU3vOENuPbaa/Hss8/atZ6/J0bPPfccAOCMM84AcHwyYe/evbjooouwbds2u+eyyy7D3Nwcvvvd765h69c/dfmr9NnPfhZbt27FhRdeiBtvvBELCwt27eXG33VVWO7//u//EGNsMR8Atm3bhv/8z/88Ra16cdLOnTtx11134Q1veAOefvppfOITn8Av/uIv4jvf+Q5mZ2cxHA6xZcuW1ne2bduG2dnZU9PgFzEpz1aat3ptdnYWZ511Vut6VVU444wzep4fB11++eV45zvfiR07duDJJ5/EH/zBH+CKK67A3r17EULo+XsClFLC9ddfj7e+9a248MILAeC4ZMLs7OyKc1yv9cS0En8B4Nd+7ddwzjnnYGZmBt/+9rfx+7//+9i/fz/+/u//HsDLj7/rCnz09MLRFVdcYb+/6U1vws6dO3HOOefgc5/7HKampk5hy3rq6cTp3e9+t/1+0UUX4U1vehNe97rX4YEHHsAll1xyClv24qPrrrsO3/nOd1o+YD29cLQaf0v/o4suugjbt2/HJZdcgieffBKve93r1rqZp5zW1bHL1q1bEUJY5mF98OBBTE9Pn6JWvTRoy5YteP3rX48nnngC09PTmEwmOHToUOuens/Pj5RnR5u309PTy5ymm6bBj370o57nz4POPfdcbN26FU888QSAnr/HSx/60Ifw5S9/GV/96lfx6le/2j4/HpkwPT294hzXaz2tzt+VaOfOnQDQmsMvJ/6uK/AxHA7x5je/Gffdd599llLCfffdh127dp3Clr346ciRI3jyySexfft2vPnNb8ZgMGjxef/+/Xjqqad6Pj8P2rFjB6anp1v8nJubw759+4yfu3btwqFDh/DII4/YPffffz9SSiaEejp++uEPf4hnn30W27dvB9Dz91hERPjQhz6EL3zhC7j//vuxY8eO1vXjkQm7du3Cv//7v7dA3r333otNmzbhjW9849p0ZJ3Ssfi7Ej322GMA0JrDLyv+nmqP1y797d/+LY1GI7rrrrvo8ccfp2uuuYa2bNnS8gDu6dj0kY98hB544AE6cOAA/cu//Avt3r2btm7dSs888wwREX3gAx+gs88+m+6//356+OGHadeuXbRr165T3Or1S4cPH6ZHH32UHn30UQJAt99+Oz366KP0/e9/n4iIPvWpT9GWLVvoS1/6En3729+mq666inbs2EGLi4v2jMsvv5x++qd/mvbt20cPPfQQnXfeefSe97znVHVpXdHR+Hv48GH63d/9Xdq7dy8dOHCA/vmf/5l+5md+hs477zxaWlqyZ/T8XZ2uvfZa2rx5Mz3wwAP09NNP27+FhQW751gyoWkauvDCC+nSSy+lxx57jL7yla/QmWeeSTfeeOOp6NK6omPx94knnqA//MM/pIcffpgOHDhAX/rSl+jcc8+lt73tbfaMlxt/1x34ICL6zGc+Q2effTYNh0N6y1veQt/4xjdOdZNedPSud72Ltm/fTsPhkF71qlfRu971LnriiSfs+uLiIn3wgx+kV77ylbRx40b6lV/5FXr66adPYYvXN331q18lAMv+XX311UTE4bY33XQTbdu2jUajEV1yySW0f//+1jOeffZZes973kOnn346bdq0id73vvfR4cOHT0Fv1h8djb8LCwt06aWX0plnnkmDwYDOOeccev/7379MIen5uzqtxFsA9Jd/+Zd2z/HIhP/+7/+mK664gqampmjr1q30kY98hOq6XuPerD86Fn+feuopetvb3kZnnHEGjUYj+smf/Em64YYb6Lnnnms95+XEX0dEtHZ2lp566qmnnnrq6eVO68rno6eeeuqpp556eulTDz566qmnnnrqqac1pR589NRTTz311FNPa0o9+Oipp5566qmnntaUevDRU0899dRTTz2tKfXgo6eeeuqpp556WlPqwUdPPfXUU0899bSm1IOPnnrqqaeeeuppTakHHz311FNPPfXU05pSDz566qmnnnrqqac1pR589NRTTz311FNPa0o9+Oipp5566qmnntaU/n8IGK9q34wGGgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sample of hypodontia\n",
    "hypodontiaimg=Image.open(\"/kaggle/working/oral-diseases/hypodontia/(25).JPG\")\n",
    "plt.imshow(hypodontiaimg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "17db0115",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T13:54:13.789162Z",
     "iopub.status.busy": "2024-01-07T13:54:13.788812Z",
     "iopub.status.idle": "2024-01-07T13:54:17.106758Z",
     "shell.execute_reply": "2024-01-07T13:54:17.104987Z"
    },
    "papermill": {
     "duration": 3.331129,
     "end_time": "2024-01-07T13:54:17.109735",
     "exception": false,
     "start_time": "2024-01-07T13:54:13.778606",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage.feature import hog\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, ExtraTreesClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from xgboost import XGBClassifier\n",
    "import time\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ea06513",
   "metadata": {
    "execution": {
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   "source": [
    "# Define the dataset directory and classes\n",
    "dataset_dir = \"/kaggle/working/oral-diseases\"\n",
    "classes = ['caries', 'gingivitis', 'toothDiscoloration', 'ulcers', 'hypodontia', 'calculus']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c2942b41",
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   "source": [
    "# Function to load images and extract features\n",
    "def load_images_and_features(dataset_dir, classes):\n",
    "    images = []\n",
    "    labels = []\n",
    "    for label, class_name in enumerate(classes):\n",
    "        class_dir = os.path.join(dataset_dir, class_name)\n",
    "        for filename in os.listdir(class_dir):\n",
    "            img_path = os.path.join(class_dir, filename)\n",
    "            img = cv2.imread(img_path, cv2.IMREAD_COLOR)\n",
    "            img = cv2.resize(img, (256, 256))\n",
    "\n",
    "            # Extract HOG features for each color channel and concatenate\n",
    "            hog_features = []\n",
    "            for channel in range(img.shape[2]):\n",
    "                channel_features, _ = hog(img[:, :, channel], orientations=8, pixels_per_cell=(16, 16),\n",
    "                                          cells_per_block=(1, 1), visualize=True)\n",
    "                hog_features.append(channel_features)\n",
    "            features = np.concatenate(hog_features, axis=0)\n",
    "\n",
    "            images.append(features)\n",
    "            labels.append(label)\n",
    "\n",
    "    return np.array(images), np.array(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "598ab814",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "outputs": [],
   "source": [
    "# Load the dataset\n",
    "X, y = load_images_and_features(dataset_dir, classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aff6dfdc",
   "metadata": {
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     "status": "completed"
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   "outputs": [],
   "source": [
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ac1e4c0d",
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   "outputs": [],
   "source": [
    "# Define classifiers\n",
    "classifiers = {\n",
    "    \"Random Forest\": RandomForestClassifier(n_estimators=100),\n",
    "    \"SVM\": make_pipeline(StandardScaler(), SVC(kernel='linear', probability=True)),\n",
    "    \"K-Nearest Neighbors\": KNeighborsClassifier(),\n",
    "    \"Naive Bayes\": GaussianNB(),\n",
    "    \"Decision Tree\": DecisionTreeClassifier(),\n",
    "    \"Logistic Regression\": make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000)),\n",
    "    \"Gradient Boosting\": GradientBoostingClassifier(n_estimators=100),\n",
    "    \"AdaBoost\": AdaBoostClassifier(n_estimators=100),\n",
    "    \"Extra Trees\": ExtraTreesClassifier(n_estimators=100),\n",
    "    \"XGBoost\": XGBClassifier(use_label_encoder=False, eval_metric='mlogloss')\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6e33d77c",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Training time for Random Forest:  86.10814142227173\n",
      "Random Forest - Accuracy: 0.5583190394511149, Precision: 0.5787614495924275, Recall: 0.5523289878700578, F1-Score: 0.549712193673363, Training Time: 86.11 seconds\n",
      "Total Training time for SVM:  1333.0890436172485\n",
      "SVM - Accuracy: 0.5660377358490566, Precision: 0.5927034187424455, Recall: 0.5851830704471608, F1-Score: 0.5867901281356814, Training Time: 1333.09 seconds\n",
      "Total Training time for K-Nearest Neighbors:  0.04128146171569824\n",
      "K-Nearest Neighbors - Accuracy: 0.5222984562607204, Precision: 0.5367588255368692, Recall: 0.5416969792624938, F1-Score: 0.5300251329034954, Training Time: 0.04 seconds\n",
      "Total Training time for Naive Bayes:  0.6105990409851074\n",
      "Naive Bayes - Accuracy: 0.44168096054888506, Precision: 0.4275260302161652, Recall: 0.43082416236535703, F1-Score: 0.42235720389146086, Training Time: 0.61 seconds\n",
      "Total Training time for Decision Tree:  105.51359367370605\n",
      "Decision Tree - Accuracy: 0.4236706689536878, Precision: 0.42879998539835573, Recall: 0.42709481547335376, F1-Score: 0.4276128349013571, Training Time: 105.51 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Training time for Logistic Regression:  230.3649923801422\n",
      "Logistic Regression - Accuracy: 0.5471698113207547, Precision: 0.5713348511160629, Recall: 0.5664151620570738, F1-Score: 0.5679735317458835, Training Time: 230.36 seconds\n",
      "Total Training time for Gradient Boosting:  14248.246028661728\n",
      "Gradient Boosting - Accuracy: 0.5617495711835334, Precision: 0.5808587558988149, Recall: 0.5561262576567042, F1-Score: 0.5620343318621561, Training Time: 14248.25 seconds\n",
      "Total Training time for AdaBoost:  970.2718508243561\n",
      "AdaBoost - Accuracy: 0.45797598627787306, Precision: 0.4414817150381096, Recall: 0.43504694575793496, F1-Score: 0.4316031112154182, Training Time: 970.27 seconds\n",
      "Total Training time for Extra Trees:  36.47813129425049\n",
      "Extra Trees - Accuracy: 0.5634648370497427, Precision: 0.5958460776430647, Recall: 0.5560649375376095, F1-Score: 0.5560719558438141, Training Time: 36.48 seconds\n",
      "Total Training time for XGBoost:  747.5342781543732\n",
      "XGBoost - Accuracy: 0.6054888507718696, Precision: 0.6269518902997163, Recall: 0.6108841571009762, F1-Score: 0.614620536171193, Training Time: 747.53 seconds\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# File path for the CSV\n",
    "results_file = 'model_evaluation_results.csv'\n",
    "\n",
    "# Check if the results file exists and load it, else create a new DataFrame\n",
    "if os.path.isfile(results_file):\n",
    "    results_df = pd.read_csv(results_file)\n",
    "else:\n",
    "    results_df = pd.DataFrame(columns=[\"Model\", \"Accuracy\", \"Precision\", \"Recall\", \"F1-Score\", \"Training Time (s)\"])\n",
    "\n",
    "# Train, evaluate each classifier, and update results\n",
    "for name, clf in classifiers.items():\n",
    "    start_time = time.time()\n",
    "    clf.fit(X_train, y_train)\n",
    "    training_time = time.time() - start_time\n",
    "    \n",
    "    print(f\"Total Training time for {name}: \", training_time)\n",
    "\n",
    "    y_pred = clf.predict(X_test)\n",
    "    \n",
    "    # Save the trained model\n",
    "    model_filename = f\"{name.replace(' ', '_')}_model.joblib\"\n",
    "    joblib.dump(clf, model_filename)\n",
    "    \n",
    "    # Calculate metrics\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    report = classification_report(y_test, y_pred, output_dict=True)\n",
    "    precision = report['macro avg']['precision']\n",
    "    recall = report['macro avg']['recall']\n",
    "    f1_score = report['macro avg']['f1-score']\n",
    "\n",
    "    # Create a temporary DataFrame\n",
    "    temp_df = pd.DataFrame({\n",
    "        \"Model\": [name],\n",
    "        \"Accuracy\": [accuracy],\n",
    "        \"Precision\": [precision],\n",
    "        \"Recall\": [recall],\n",
    "        \"F1-Score\": [f1_score],\n",
    "        \"Training Time (s)\": [training_time]\n",
    "    })\n",
    "\n",
    "    # Append to existing DataFrame and save\n",
    "    results_df = pd.concat([results_df, temp_df], ignore_index=True)\n",
    "    results_df.to_csv(results_file, index=False)\n",
    "\n",
    "    # Print report\n",
    "    print(f\"{name} - Accuracy: {accuracy}, Precision: {precision}, Recall: {recall}, F1-Score: {f1_score}, Training Time: {training_time:.2f} seconds\")\n",
    "\n",
    "    # Confusion Matrix\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    plt.figure(figsize=(10, 7))\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['caries', 'gingivitis', 'toothDiscoloration', 'ulcers', 'hypodontia', 'calculus'],yticklabels=['caries', 'gingivitis', 'toothDiscoloration', 'ulcers', 'hypodontia', 'calculus'])\n",
    "    plt.title(f'Confusion Matrix for {name}')\n",
    "    plt.ylabel('Actual')\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.savefig(f\"confusion_matrix_{name}.png\")\n",
    "    plt.close()\n"
   ]
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 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 2729190,
     "sourceId": 5797207,
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